smart healthcare research paper

Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics

PSU Research Review

ISSN : 2399-1747

Article publication date: 17 October 2019

Issue publication date: 15 September 2020

The aim of this paper is to identify some of the challenges that need to be addressed to accelerate the deployment and adoption of smart health technologies for ubiquitous healthcare access. The paper also explores how internet of things (IoT) and big data technologies can be combined with smart health to provide better healthcare solutions.

Design/methodology/approach

The authors reviewed the literature to identify the challenges which have slowed down the deployment and adoption of smart health.

The authors discussed how IoT and big data technologies can be integrated with smart health to address some of the challenges to improve health-care availability, access and costs.

Originality/value

The results of this paper will help health-care designers, professionals and researchers design better health-care information systems.

• Internet of things
• Connected health
• Smart health
• Digital health

Zeadally, S. , Siddiqui, F. , Baig, Z. and Ibrahim, A. (2020), "Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics", PSU Research Review , Vol. 4 No. 2, pp. 149-168. https://doi.org/10.1108/PRR-08-2019-0027

Emerald Publishing Limited

Copyright © 2019, Sherali Zeadally, Farhan Siddiqui, Zubair Baig and Ahmed Ibrahim.

Published in PSU Research Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Enhancing the quality of health care and improving ease of access to health records while maintaining reasonable costs is challenging for health-care organizations globally ( iScoop, 2018 ). The problem is further exacerbated by the rapidly increasing world population, especially the rate of increase of senior people (65 years old and higher). According to the World Health Organization ( WHO, 2018 ), the number of senior people will increase to about 1.5 billion by 2050. An aging population implies increase in chronic diseases that require frequent visits to health-care providers, as well as increased hospitalization needs. The rise in the number of patients requiring constant care significantly increases medical treatment costs. For example, in the USA, the cost of health care was about 17.9 per cent of the gross domestic product in 2017 ( CMS, 2019 ) and is expected to hit 19.4 per cent in 2027 ( HealthAffairs, 2019 ). Figure 1 shows the national health-care costs in the USA over a period of about 45 years.

Over the past few decades, Information and Communication Technologies (ICT) have been widely adopted in the health-care environment to make health-care access and delivery easier and most cost-effective. The use of ICT has led to the development of electronic health record (EHR) systems. EHRs contain complete patient health history (current medications, immunizations, laboratory results, current diagnosis, and so on) and can be easily shared among various providers. They have shown to enhance patient-provider interaction ( Haluza and Jungwirth, 2014 ). The adoption of ICT in the health sector is generally referred to as digital health care ( BroadbandCommission, 2017 ).

Over the years, digital health care has extended from primarily maintaining electronic patient data and providing patient Web portals, to allowing further flexibility and convenience in health-care management, and is commonly referred to as connected health ( Loiselle and Ahmed, 2017 ; IHS, 2015 ; Cisco, 2019 ). Connected health uses smart phones and mobile applications, together with wireless technologies (such as Bluetooth, Wi-Fi and long-term evolution) to allow patients to connect readily with their providers without visiting them frequently. For example, a typical hypertensive patient would see his/her doctor once in six months to report daily blood pressure readings. With a monitoring application, the patient can transmit daily or weekly blood pressure readings thereby enabling his/her doctor to detect a problem and intervene earlier.

Connected health has evolved into smart health wherein conventional mobile devices (such as smart phones) are used together with wearable medical devices (such as blood pressure monitors, glucometers, smart watches, smart contact lenses, and others) and internet of things (IoT) gadgets (such as implantable or ingestible sensors) to enable continuous patient monitoring and treatment even when patients are at their homes ( Uddin et al. , 2017 ; Zilani et al. , 2018 ). Smart health is expected to keep hospitalization expenses low and provide timely treatment for various medical conditions ( Sharma et al. , 2017 ) by placing IoT sensors on health monitoring equipment. The information collected by these microchips can then be sent to any remote destination ( Chaudhury et al. , 2017 ). For example, wearable sensors (such as a temperature sensor and the heartbeat sensor) can act as data collecting units, collecting the physiological signals from the patient’s body. The collected data are then forwarded to a local gateway server via a Wi-Fi network such that end-systems (such as a physician’s laptop) can retrieve the collected data from the gateway server. Regular server updates allow physicians access to real-time patient data. These devices work together to create a unified medical report that can be accessed by various providers. This data is not only useful for the patient, but can be pooled together to study and predict health-care trends across cultures and countries. Figure 2 illustrates an example of a smart health-care system.

The amount of data that may be generated as a result of combining smart health devices with IoT sensors is massive. Such data are often referred to as “big data.” Application of effective analytic technologies to Big Data can help provide meaningful information to physicians which would help them make more timely, informed decisions as well as take proactive measures for better health management ( Johri et al. , 2017 ).

1.1 Main contributions of this work

In this paper, we identify technical challenges that are hindering the wide-scale adoption of smart and connected health-care systems. We also discuss how big data and IoT technologies can accelerate the speed at which connected health care can be implemented, deployed and adopted by all health-care stakeholders.

The rest of this paper is organized as follows. In Section 2 we present some of the current challenges to digital health-care adoption. Section 3 discusses how IoT and big data technologies can help promote digital health-care adoption and improve health-care efficiency. Finally, we make some concluding remarks in Section 4.

2. Challenges in digital health-care adoption

Digital health-care systems that leverage EHRs and use technologies such as IoT and big data are expected to seamlessly connect patients and providers across diverse health-care systems. These systems are also being increasingly connected via the Internet to various types of medical wearable technologies that are being worn for real-time health-care monitoring. Figure 3 shows the percentage of population (in millions) adopting the medical wearable technology.

However, several challenges need to be addressed before digital health care can develop stable, flexible and interoperable systems. Next we discuss some of the current challenges that are hindering the widespread adoption of digital health care ( Firouzi et al. , 2018 ).

2.1 Security and privacy

IoT devices can pose a threat to users’ security and privacy. Unauthorized access of IoT devices could create a serious risk to patients’ health as well as to their private information ( Zeadally et al. , 2016 ). Connected gadgets including medical and mobile devices capture, aggregate, process and transfer medical information to the cloud. The device layer is vulnerable to tag cloning, spoofing, RF jamming, and cloud polling. In cloud polling, traffic is redirected to allow command injections directly into a device through a man-in-the-middle attack. A direct connection attack involves the use of a service discovery protocol such as universal plug and play, or properties of Bluetooth low energy (BLE), to locate and target IoT devices.

Denial of service (DoS) attacks can affect health-care systems and affect patient safety. While a common defense to DoS is redundancy (the use of multiple devices on the network), in a health-care environment the duplication of resources may not always be possible because some of the gadgets are implanted life-critical systems. The fast detection of potential security threats remains a challenge because of the number and complexity of emerging software and hardware vulnerabilities. This issue is getting worse as increasing number of devices are being connected to the Internet. Today, default authentication remains prevalent, and insecure Web-based interface access further increases the attack surface. Additionally, we have also seen a surge in the proliferation of wearable devices (including different types of embedded sensors and implanted medical devices) in recent years. The lack of security standards of these devices along with the availability of powerful search engines such as Shodan (2019) which enables locating Internet-connected devices ( Williams and McCauley, 2016 ), make these wearable devices vulnerable to all kinds of attacks ( Das et al. , 2018 ).

Recently, many wireless networking technologies have also been deployed in the health-care environment and these include Wi-Fi, BLE and ZigBee that are being used to provide connectivity to different types of medical devices and sensors ( Zeadally and Bello, 2019 ). Security protection of these wireless and sensor technologies against eavesdropping, Sybil attacks, sinkhole attacks, and sleep deprivation attacks must be enforced. Centralized data sets of personal information, family history, electronic medical records and genomic data, should also be protected from hackers and malicious software to enforce security and privacy ( Nambiar et al. , 2017 ).

Confidentiality and privacy are important concerns for physicians as well. Patients may not want to share their medical records because of the sensitive nature of the health data (for example, cancer or HIV test results). Concerns exist that the integration of connected technology into current medical information systems may compromise the confidentiality of health data ( Sonune et al. , 2017 ). These privacy concerns stem from the fear that digital and connected technology may attract hackers. Furthermore, researchers sometimes argue that connected health technology would be implemented imperfectly, allowing for security vulnerabilities to be exploited ( Poyner and Sherratt, 2018 ). Privacy concerns increase when the patient’s information is shared among several applications. Low security and misconfigured device and network settings could affect the privacy of patients and their data. Additional risks arise because of linking geographical location with purchases from pharmacies which may provide a profile of an individual’s health status. Another concern is the use of various providers which are mandated to submit confidential data to law enforcement agencies. This can affect the adoption and use of the technology where patients are concerned about privacy. The networks which transmit data are often highly heterogeneous and are frequently managed by third parties which makes the protection of security and privacy as well as governance of this data even more challenging ( Williams and McCauley, 2016 ).

2.2 Inter-realm authentication and interoperability issues

Inter-realm authentication is essential for entities operating in different domains to establish trust for carrying out digital health transactions. Shibboleth is a federated identity solution that facilitates entity authentication both within and between organizational systems ( Shibboleth, 2019 ). At a country level, Shibboleth, a system that provides inter-realm authentication has been deployed and tested successfully. A typical Shibboleth-based system enables a user of a digital health system to authenticate itself to an identity provider (IdP), and subsequently sends a service request for a service hosted on a service provider (SP). The IdP and SP share the user’s identity information in the background. Through such a federated arrangement, Shibboleth facilitates single sign-on capabilities for digital health entities as in ( eHealth, 2019 ).

Shibboleth-based systems are secure and provide strong authentication across multiple realms of a digital health system. However, not all digital health systems have Shibboleth implementations owing to the lack of facilities to host separate Identity and Service Providers in an organization within a nation, and to have these hosted across all similar digital health organizations. The lack of information technology (IT) skills and necessary funding especially in the third world, hinders the ready adoption of systems such as Shibboleth.

Another aspect that requires attention as a prevailing digital health issue for nations is the lack of interoperability between nations intending to cooperate on digital health ICT infrastructures. This shortcoming is due to not only the limited ICT infrastructures or dearth of IT skills, but also the lack of policy for global cooperation among nations on the exchange of sensitive medical data, which would facilitate telemedicine and provisioning of high-quality medical care remotely.

Projects such as Liberty Alliance ( Broda, 2007 ) have fostered bringing together disparate platforms and standards for inter-realm authentication under one umbrella. These platforms and standards are: OpenID, inames, Openliberty, World Wide Web Consortium, Organization for the Advancement of Structured Information Standards (OASIS) and Liberty Alliance Project. The proposal of the Liberty Alliance Project aims to enable interoperability between standards at the Internet Identity Layer. They have also highlighted the need for collaboration among various stakeholders through public forums and certification programs.

Authentication of medical doctors against hospital systems is achieved by using a user ID and a password or an X.509 digital certificate.

Hospitals send a Security Assertion Markup Language (SAML) ( SAML, 2019 ) assertion to Catalan health services for each new prescription.

Pharmacies use X.509 digital certificates or user credentials (ID/password) to authenticate against the Catalan Council of Pharmacies database.

For dispensing new medicines, the Catalan Council of Pharmacies sends an SAML assertion to Catalan Health Services, to give access to pending ePrescriptions.

ePrescriptions are also to be signed by the doctors and dispensed medicines are reported to the Catalan Health Service.

The level of confidentiality of digital health attribute assertions is entirely dependent on the strength of the cipher being used.

Targeted confidential messages cannot be crafted unless a holistic certification mechanism is in place to issue and maintain public-private key pairs to facilitate data encryption and decryption.

Anonymity of subjects is not the same as pseudonymity. Consequently, the ability of the SAML-based digital health authentication system to ensure that users remain anonymous, is restricted, because of the limitation of the SAML standard.

The original SAML specification is vulnerable to collusion-based attacks, wherein two or more malicious system entities cooperate to share information exchanged from previous transactions, and consequently compromise the confidentiality of messages exchanged.

Recent implementations of federated identification and authentication based on SAML include the presence of a CA to facilitate public key-based data encryption and/or data integrity verification. However, the scope of verification of an entity’s identity will only be limited to within the zone covered by the CA. In particular, a digital health system that relies on the presence of a CA within a geographical bound such as city or state limits, will not be able to provide authentication services for other entities outside the CA bounds.

2.3 Health information exchange barriers

Health Information Exchange (HIE) enhances health-care delivery by providing the ability to electronically share health-care information among diverse health-care organizations in a reliable and secure manner. Currently, HIE is implemented by using one of the following methods: consumer-mediated exchange, directed exchange, and query-based exchange ( Williams et al. , 2012 ).

Consumer-mediated exchange provides patients with access to their own electronic records, thus allowing them to track their health conditions, determine whether there is erroneous billing or medical data, and update their self-reports. Directed exchange is conducted when a health-care organization transfers such vital information such as laboratory test results and medication dosage to other specialists involved in the care of the same patient. Query-based exchange usually occurs in unplanned medical care when a health-care organization needs the previous health records of a new patient. This is done by requesting access to these records through the HIE system.

Impediments in the deployment of HIE systems are mainly owing to security and privacy concerns. Some of the issues associated with current HIE systems are as follows: First, abuse of access rights by authorized insiders ( Szerejko, 2015 ). This usually happens when health-care organizations share medical records of their patients with unauthorized individuals, either out of irresponsibility, for personal reasons, or in exchange for some kind of gain. For instance, medical records of celebrities and politicians frequently leak out of Healthcare Information Management Systems (HIMSs) into the media. Second, violation of rules by unauthorized insiders, who may have access to the system itself but not to the records ( Strauss et al. , 2015 ). For instance, hospital employees who do not provide direct patient care or former employees who have not yet been electronically restricted from data retrieval. The former group can use the existing access to hack the private informational database while the latter may decide to seek vengeance on their former employers by undermining the HIMS’s security. Third, unauthorized intruder attempts to enter the system either by attacking it directly or by pretending to be part of the health-care team ( Saiz et al. , 2014 ). Figure 4 shows the increase in the number of breaches according to “Unauthorized Access/Disclosure” and “Hacking/IT Incident” between 2010 and 2015 according to the numbers published by the United States Department of Health and Human Services (DHHS) ( US DHHS, 2019 ).

The emergence of health care-related cybercrime is a major concern and an emerging threat to HIMSs ( Agha, 2015 ). Security breaches in hospitals can cost them as much as $7 million in terms of damaged reputation, fines, litigation and so on ( Claunch and McMillan, 2013 ). Major breaches have occurred in organizations such as Anthem, CareFirst, Premera and UCLA Health systems. As a result, a total of 143 million patient records were exploited by cyberattacks, which amounted to 45 per cent of the American population ( iSheriff, 2015 ) as shown in Figure 5 .

A cyber security assessment by the Healthcare Information and Management Systems Society in 2015 showed that in the previous 12 months, 64 per cent of health-care organizations had been exposed to external cyberattacks ( Mohammed et al. , 2015 ). Bloomer News claimed that in the previous 2 years, of all health-care organizations, 90 per cent have been attacked ( Pettypiece, 2015 ). Furthermore, most data breaches occur in health-care and medical industries as compared to financial, governmental, or educational sectors ( Gleeson and Friel, 2013 ).

2.4 Device communication

One of the major challenges to implementing smart or connected health is communication. Many devices now have sensors to collect data and they often communicate with the server in their own language. Each manufacturer has its own proprietary protocol, which means sensors made by different manufacturers cannot necessarily communicate with each other. This fragmented software environment, coupled with privacy concerns, frequently isolates valuable information on data islands, undermining the main idea behind IoT ( Dimitrov, 2016 ).

The presence of several devices also opens up concerns related to connecting medical devices using wireless network technologies. For instance, people using a Wireless Personal Area Network (WPAN)-enabled device are expected to move freely but mobility can result in collisions when WPANs that operate in similar frequency channel are within close range. Collision in WPANs has several disruptive effects because it reduces performance and may lead to disastrous situations especially when health-care delivery is concerned. Therefore, it is essential to make sure that medical devices operate properly when connected using various types of wireless communication technologies ( Gawanmeh, 2016 ).

Smart health systems are not always easy to use by physicians. The presence of a large number of features could sometimes make a system complex which in turn demotivates health-care workers in learning how to use it ( Grood et al. , 2016 ).

Users and service providers both require interoperability within individual IoT domains and amongst themselves. This creates complex challenges because the various disciplines captured by IoT are regulated by a diverse group of regulatory agencies. This complexity is further exacerbated in connected health scenarios wherein medical standards require particularly strict regulations. Companies that want to build smart health applications in the medical area must consider the regulations imposed by Food and Drug Administration, the Centers for Medicare and Medicaid Services, and the Federal Communications Commission (FCC) ( Firouzi et al. , 2018 ).

A truly interoperable connected health system is one in which data flows with both one-to-one and one-to-many connections, leading to the exchange of information among multiple interfaces which require systems to cooperate with one another. In health-care environments, it is important for devices to be compatible with many transmission formats and protocols for authentication and encryption. Device management will require directories of devices’ functionality, protocols, terminologies and standards compliance. The level of “plug and play” interoperability now commonplace in non-health areas remains a challenge for medical devices ( Williams and McCauley, 2016 ).

2.5 Collection and management of data

Digital health care that leverages IoT sensor devices faces several data management challenges. The data originates from medical sensors, which are worn or implanted inside the human body. Because the state of the human body is constantly changing, there is a continuous influx of data that is being produced. Furthermore, the captured data are heterogeneous (consisting of various data formats). For example, Electro Cardio Graph (ECG) data are often encoded in XML format, while data received from camera-based IoT devices is typically recorded in a wide variety of image formats. A connected health scenario consists of heterogeneous connected components including user devices, networks, systems (with large data volumes), variety and velocity (collected from various sources), and veracity (uncertain data). Digital health systems need to be designed using suitable data-driven learning techniques to handle its continuously varying cyber-physical components. Proper analysis of data can give valuable information about patients’ health conditions. When a lot of patients’ data are not analyzed and knowledge is not extracted from it, we do not reap the maximum usefulness of this data, and its collection also wastes computing resources. Over the past decade, various data analysis tools have been developed by researchers, pharmaceutical companies, and health-care providers ( Nambiar et al. , 2017 ) to enable the fast extraction of useful knowledge from patients’ data. Several challenges exist because of the absence of standardized data collection formats as well as the volume and velocity of data generated in health-care settings. Integrity is also crucial with respect to big data. Inaccurate data can lead to incorrect decisions and long term strategic planning. Because health-care data often comes from various sources, robust authentication systems are needed to ensure that health-care data are submitted from actual registered clinics, hospitals, and medical institutions ( Tse et al. , 2018 ).

Collecting data that is clean, formatted, thorough, and precise in a health-care system is challenging ( Anagnostopoulos et al. , 2016 ). In addition, health-care definitions are complex and metrics are constantly changing in the health-care industry. For example, the length of stay (LOS) metric is a key financial measure that is also reported by clinicians. The LOS definitions can vary and decisions can be skewed if users either do not know which metric to use or do not know the definition of the metric that was reported. Clinicians calculate LOS by how long a patient physically stays in the bed. But from a financial perspective, LOS is calculated on a 24-hour scale that ends at midnight. As a result of the discrepancy in LOS definitions, the data recorded could be incorrectly interpreted ( Burke, 2015 ).

The complexity of data in the health-care industry makes integrating big data challenging. While some information, such as health variables, have to be updated frequently, more passive information such as geographic location and contact information need not be updated that often. Data integrity should be maintained while updating information. Inappropriate document control may pose a risk to data integrity. Maintaining these databases is challenging because of the costs of maintenance as well as HIPAA regulations ( Hipaa, 2019 ) rules.

2.6 Design and implementation based on multi-disciplinary knowledge

Digital health (including connected and smart health) is developed using expertise in many fields including embedded systems, network design, data analytics and bioengineering. The design and implementation of such a heterogeneous system requires extensive knowledge in multi-disciplinary areas. The system also needs to evolve continuously to address constantly changing needs. For example, currently there is limited integration of smart health systems with some medical systems such as ultrasound and CAT scan imaging.

3. Improving the adoption of digital health care with internet of things and big data technologies

In this section, we discuss some of the ways in which IoT and big data can together help improve the adoption of smart health which will result in improved health-care delivery and access.

3.1 Evidence-based care

The exponential increase in the volume of health-care data generated by IoT devices makes data processing very challenging. Big data can provide evidence-based care by aggregating data sets from diverse sources. Analysis of data can provide useful insights into detecting anomalies and providing appropriate treatments to patients. Intelligent analysis using new methods can provide substantial financial savings on the order of several hundred billion dollars, which amounts to about 8 per cent of the national health expenses ( Olaronke and Oluwaseun, 2016 ).

The study of health-related information with efficient methods promotes early identification of disease patterns, which expands public health surveillance. This ensures that appropriate and timely decisions on the treatment of a particular disease are taken thereby reducing patient mortality. Big data enhances the type of care patients receive as treatment decisions are based upon knowledge gathered from analyzing large data sets.

3.2 Self-learning and self-improvement

IoT sensors enable data collection, but IoT alone cannot provide rehabilitation treatments. Accurate and timely treatments can be made based on fast patient evaluation, and the development of rehabilitation procedures corresponding to the medical investigation. Many factors need to be considered to provide a precise treatment. Computer tools merely rely on the data collected by the sensors and past case studies, while self-learning techniques can adaptively analyze and recommend new treatment options. A few self-learning algorithms [including artificial neural network (ANN), genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA)], are suitable for data analysis and mining. Topology-based and ontology-based heuristic algorithms can help in finding optimal solutions for a large-scale health-care system ( Yuehong et al. , 2016 ).

Various distributed computing platforms are being used today for big data analytics. These platforms include Apache Samza, Apache Spark, Hadoop MapReduce, Apache Storm and Flink. Hadoop MapReduce and Apache Spark are the most widely used platforms for massive data storage and analysis ( Praveena and Bharathi, 2017 ). Hadoop is an easy to use open-source tool for handling big data applications. The Hadoop MapReduce framework provides a major distributed computing platform that is capable of storing and processing large amounts of unstructured data sets ( Khan and Iqbal, 2017 ).

MapReduce ( Merla and Liang, 2017 ) is a programming environment that permits parallel and distributed processing on huge amounts of data on large clusters of hardware. Hive ( Garg, 2015 ) is the structured query language (SQL)-like bridges that permit predictable business applications to run SQL queries against a Hadoop cluster. PIG ( Jain and Mayrya, 2017 ) is a tool that makes Hadoop more usable by making MapReduce queries simpler to implement. Wibidata ( Moorthy et al. , 2014 ) is a tool that integrates Hadoop with Web analytics to optimize data usage by websites. It is a platform that automatically maps user's queries to Hadoop jobs. Rapidminer ( Dwivedi et al. , 2016 ) provides an integrated platform for analytics (both business and predictive), mining of data and machine learning.

3.3 Standardization

Various organizations (such as IEEE, IETF, ITU-T) have contributed to the deployment and standardization of IoT technologies. The standardization of IoT ( Stuurman and Kamara, 2016 ) ( Singh et al. , 2017 ) was mostly influenced by the recommendations provided by the Machine-to-Machine European Telecommunications Standards Institute (ETSI) and Internet Engineering Task Force (IETF) Working Groups. All new and emerging ideas should be integrated to form a global solution that helps build standardizations for the future Internet. Based on the results provided by the CERP-IoT project ( IERC, 2016 ), future Internet is an extension of the existing one by integrating general things into wider networks. The standardization will enable the development of IoT-based health-care systems. Table I lists various standardization bodies and some of their recent IoT standards related work.

3.4 Privacy and security

IoT-based systems are useful as long as its users remain safe. In IoT systems, all types of data collection and mining are performed over the Internet. Thus, personal data can be accessed at various stages (during collection, transmission and so on). Patients’ safety should be taken into consideration by preventing any form of tracking or illegal identification. The higher the level of autonomy and intelligence of the IoT devices, the harder the protection of identities and privacy becomes. IoT-based applications are also vulnerable because of wireless communication which makes eavesdropping easier. Additionally, IoT devices generally have low energy and low computing power which makes it harder to implement complex algorithms to guarantee security. As big data becomes more ubiquitous in the health-care system, more security challenges will emerge. Rigorous research is needed to ensure privacy, trust, and security throughout the health-care environment.

3.5 Interactive reporting and visualization

Big data applications need to distinguish between analysis and reports ( Suyts et al. , 2017 ). Big data applications will not succeed if data are simply written to reports. Applications need to derive valuable insights from a bulk of data and only mention specific highlights ( intelliPaat, 2019 ). It is also necessary to train algorithms to generate precise insights based on available data without which the credibility of the report comes into question. Reports can be made appealing and useful by including graphs and statistical information. Applications should also focus on developing visualizations that would make it easy to derive insights from a report and allow easy identification of trends and challenges in a health-care segment.

As discussed above, there are several challenges that still need to be addressed before digital health care can be widely adopted. Table II summarizes some of these challenges together with possible solutions.

4. Conclusion

We are currently witnessing rapid advances in information communication technologies. It is a well-known fact that the implementation and deployment of these technologies in the health-care sector bring about significant benefits (affordable health care, cost-efficient health services, and many others) to all health-care stakeholders. In this work, we discussed some of the major impediments that are slowing down digital health-care adoption nationally and internationally along with some possible solutions to enable faster digital health-care deployment. While the health-care sector is increasingly interested in leveraging IoT and big data technologies to become more efficient, there are several challenges that need to be addressed before digital health care can become a widespread reality.

National Health Expenditure (percentage of GDP)

A smart health-care system

US adult wearable users and market penetration, 2016-2021 (millions and percentage of population)

Number of breaches between 2010 and 2015 according to the DHHS

Total number of patient records breached between 2009 and 2015 by cyberattacks according to DHHS

Standards organizations and IoT-related work

Digital health-care adoption: challenges and solutions

* In this paper we will use the terms digital health, connected health and smart health interchangeably.

Agha , L. ( 2015 ), “ The effects of health information technology on the costs and quality of medical care ”, Journal of Health Economics , Vol. 34 , pp. 19 - 30 , available at: www.sciencedirect.com/science/article/pii/S0167629613001720

Anagnostopoulos , I. , Zeadally , S. and Exposito , E. ( 2016 ), “ Handling big data: research challenges and future directions ”, Journal of Supercomputing , Vol. 72 No. 4 , pp. 1494 - 1516 .

BroadbandCommission ( 2017 ), “ Digital health: a call for government leadership and cooperation between ICT and health ”, available at: www.broadbandcommission.org/Documents/publications/WorkingGroupHealthReport-2017.pdf (accessed August 2019 ).

Broda ( 2007 ), Managing Trust in e-Health with Federated Identity Management , eHealth Workshop , Konolfingen .

Burke , J. ( 2015 ), “ Is that data valid? Getting accurate financial data in healthcare ”, Health Catalyst , available at: www.healthcatalyst.com/financial-data-in-healthcare-edw (accessed August 2019 ).

catCert ( 2017 ), “ Identity and capability management in eHealth: the CATCert approach ”, available at: www.projectliberty.org/liberty/content/download/3691/24338/file/071011%20I%20Alamillo%20CATCert%20v1r0%20case%20study.pdf (accessed August 2019 ).

Chaudhury , S. , Paul , D. , Mukherjee , R. and Haldar , S. ( 2017 ), “ Internet of thing based HealthCare monitoring system ”, The 8th IEEE Annual Conference on Industrial Automation and Electromechanical Engineering , Bangkok .

Cisco ( 2019 ), “ Making connected health a reality ”, Cisco Systems , available at: www.cisco.com/c/dam/en_us/solutions/industries/docs/healthcare/connected_health_brochure.pdf (accessed August 2019 ).

Claunch , D. and McMillan , M. ( 2013 ), “ Determining the right level for your IT security investment ”, Healthcare Financial Management , Vol. 67 No. 5 , pp. 100 - 104 .

CMS ( 2019 ), “ National health expenditure data ”, Centers for Medicare and Medicaid Services , available at: www.cms.gov/ (accessed August 2019 ).

Das , A.K. , Zeadally , S. and He , D. ( 2018 ), “ Taxonomy and analysis of security protocols for internet of things ”, Future Generation Computer Systems , Vol. 89 , pp. 110 - 125 .

Dimitrov , D.V. ( 2016 ), “ Medical internet of things and big data in healthcare ”, Healthcare Informatics Research , Vol. 22 No. 3 , pp. 156 - 163 , available at: www.ncbi.nlm.nih.gov/pmc/articles/PMC4981575/

Dwivedi , S. , Kasliwal , P. and Soni , S. ( 2016 ), “ Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime) ”, IEEE Symposium on Colossal Data Analysis and Networking (CDAN) , Indore .

eHealth ( 2019 ), “ Cookbook identity and authorization management ”, available at: www.ehealth.fgov.be/ehealthplatform/nl/search?q=i.am%20overview&filter=&doctype%5BCookbook%5D=on&page=1&filter%5Bbase_services%5D=on

eMarketer ( 2017 ), “ Wearables still far from mass adoption ”, available at: www.emarketer.com/content/wearables-still-far-from-mass-adoption (accessed August 2019 ).

ETSI TR 103 394 ( 2018 ), “ Smart body area networks (SmartBAN); system description ”, available at: www.etsi.org/committee/1413-smartban (accessed August 2019 ).

ETSI ( 2019 ), “ European telecommunications standards institute ”, available at: www.etsi.org/ (accessed August 2019 ).

Firouzi , F. , Farahani , B. , Ibrahim , M. and Chakrabarty , K. ( 2018 ), “ From EDA to IoT eHealth: promise, challenges, and solutions ”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , Vol. 37 No. 12 , pp. 2965 - 2978 .

Garg , V. ( 2015 ), “ Optimization of multiple queries for big data with Apache Hadoop/Hive ”, IEEE International Conference on Computational Intelligence and Communication Networks , Jabalpur .

Gawanmeh , A. ( 2016 ), “ Open issues in reliability, safety, and efficiency of connected health ”, First IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies , Washington, DC .

Gleeson , D. and Friel , S. ( 2013 ), “ Emerging threats to public health from regional trade agreements ”, The Lancet , Vol. 381 No. 9876 , pp. 1507 - 1509 .

Gomez , C. , Tian , H. , Cao , Z. and Kovatsch , M. ( 2018 ), “ Energy-efficient features of internet of things protocols ”, IETF draft , available at: https://tools.ietf.org/id/draft-ietf-lwig-energy-efficient-08.html (accessed August 2019 ).

Gopi , P. and Hwang , T. ( 2016 ), “ BSN-care: a secure IoT-based modern healthcare system using body sensor network ”, IEEE Sensors Journal , Vol. 16 No. 5 , pp. 1368 - 1376 .

Grood , C. , Raissi , A. , Kwon , Y. and Santana , M.J. ( 2016 ), “ Adoption of e-health technology by physicians: a scoping review ”, Journal of Multi-Disciplinary Health , Vol. 9 , p. 335 , available at: www.ncbi.nlm.nih.gov/pmc/articles/PMC4975159/

Haluza , D. and Jungwirth , D. ( 2014 ), “ ICT and the future of healthcare: aspects of doctor-patient communication ”, International Journal of Technology Assessment in Health Care , Vol. 30 No. 3 .

HealthAffairs ( 2019 ), “ National health expenditure projections, 2018–27: economic and demographic trends drive spending and enrollment growth ”, available at: www.healthaffairs.org/doi/full/10.1377/hlthaff.2018.05499 (accessed August 2019 ).

Hipaa ( 2019 ), “ Health information privacy ”, available at: www.hhs.gov/hipaa/index.html (accessed August 2019 ).

IEEE 2030.5 ( 2013 ), “ Adoption of smart energy profile 2.0 application protocol standard ”, available at: https://standards.ieee.org/standard/2030_5-2013.html (accessed August 2019 ).

IEEE ( 2019 ), “ The world's largest technical professional organization for the advancement of technology ”, available at: www.ieee.org/ (accessed August 2019 ).

IEEE-1073-10103 ( 2012 ), “ Health informatics–point-of-care medical device communication ”, available at: https://standards.ieee.org/standard/11073-10103-2012.html (accessed August 2019 ).

IEEE-802.15.4 ( 2013 ), “ IEEE standard for local and metropolitan area networks – part 15.4: Low-Rate wireless personal area networks (LR-WPANs) ”, https://standards.ieee.org/standard/802_15_4j-2013.html (accessed August 2019 ).

IERC ( 2016 ), “ IoT European Research Center ”, available at: www.internet-of-things-research.eu/ (accessed August 2019 ).

IETF ( 2019 ), “ Internet engineering task force ”, available at: www.ietf.org/ (accessed August 2019 ).

IHS ( 2015 ), “ The connected patient ”, available at: https://cdn.ihs.com/www/pdf/Technology-White-Paper-The-Connected-Patient.pdf (accessed August 2019 ).

intelliPaat ( 2019 ), “ What is data analytics ”, available at: https://intellipaat.com/blog/what-is-data-analytics/ (accessed August 2019 ).

iScoop ( 2018 ), “ Healthcare in digital transformation: digital and connected healthcare ”, available at: www.i-scoop.eu/digital-transformation/healthcare-industry/ (accessed August 2019 ).

iSheriff ( 2015 ), “ The new heathcare crisis: cybercrime, patient records and information security ”, White Paper .

ITU ( 2019 ), “ ITU telecommunication standardization sector ”, available at: www.itu.int/en/ITU-T/Pages/default.aspx (accessed August 2019 ).

ITU-T ( 2018 ), “ Series Y: global information infrastructure, internet protocol, aspects, next-generation networks, internet of things and smart cities ”, available at: www.itu.int (accessed August 2019 ).

Jain , P. and Mayrya , J.P. ( 2017 ), “ Comparative analysis using hive and pig on consumers data ”, International Journal of Computer Science and Information Technologies , Vol. 8 , No. 2 , pp. 285 - 291 .

Johri , P. , Singh , T. , Das , S. and Anand , S. ( 2017 ), “ Vitality of big data analytics in healthcare department ”, IEEE International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) , Dubai .

Khan , M. and Iqbal , N. ( 2017 ), “ Computational performance analysis of cluster-based technologies for big data analytics ”, The IEEE International Conference on Internet of Things (iThings) , IEEE , Exeter , pp. 280 - 286 .

Loiselle , C.G. and Ahmed , S. ( 2017 ), “ Is connected health contributing to a healthier population? ”, Journal of Medical Internet Research , Vol. 19 No. 11 , p. e386 .

Merla , P. and Liang , Y. ( 2017 ), “ Data analysis using Hadoop MapReduce environment ”, IEEE International Conference on Big Data , Boston, MA .

Mohammed , D. , Mriani , R. and Mohammed , S. ( 2015 ), “ Cybersecurity challenges and compliance issues within the US healthcare sector ”, International Journal of Business and Social Research , Vol. 5 No. 2 , pp. 55 - 66 .

Moorthy , M. , Baby , R. and Senthamaraiselvi , S. ( 2014 ), “ An analysis for big data and its technologies ”, International Journal of Computer Science Engineering and Technology , Vol. 4 No. 12 .

Nambiar , A.R. , Reddy , N. and Dutta , D. ( 2017 ), “ Connected health: opportunities and challenges ”, IEEE International Conference on Big Data , IEEE , Boston, MA , pp. 1658 - 1662 .

Oasis ( 2005 ), “ Security and privacy considerations for the OASIS security assertion markup language (SAML) V2.0 ”, OASIS Standard .

OCF ( 2019 ), “ Open connectivity foundation ”, available at: https://openconnectivity.org/ (accessed August 2019 ).

OCF-Security ( 2019 ), “ Cloud security specification ”, available at: https://openconnectivity.org/draftspecs/Essen/CR%202871%20-%20OCF_Cloud_Security_Specification.pdf (accessed August 2019 ).

Olaronke , I. and Oluwaseun , O. ( 2016 ), “ Big data in healthcare: prospects, challenges and resolutions ”, Future Technologies Conference , IEEE , San Francisco, CA , pp. 1152 - 1157 .

Peterson , P.G. ( 2019 ), “ Growing healthcare costs in the US ”, Peter G. Peterson Foundation , available at: www.pgpf.org/chart-archive/0056_health-care-costs-proj (accessed August 2019 ).

Pettypiece , S. ( 2015 ), “ Rising cyber attacks costing health system $6 billion annually ”, available at: www.bloomberg.com/news/articles/2015-05-07/rising-cyberattacks-costing-health-system-6-billion-annually (accessed August 2019 ).

Poyner , I.K. and Sherratt , R.S. ( 2018 ), “ Privacy and security of consumer IoT devices for the pervasive monitoring of vulnerable people ”, IET Living in the Internet of Things: Cybersecurity of the IoT , London .

Praveena , M.A. and Bharathi , B. ( 2017 ), “ A survey paper on big data analytics ”, IEEE International Conference on Information, Communication, and Embedded Systems , Chennai .

Saiz , C.P. , Markina , I.C. , Yarza , A.A. , López , M.R. and Eizaguirre , L.E. ( 2014 ), “ Disclosure of health information: a challenge of trust between the various sectors involved ”, Revista Latina de Comunicacíon Social , Vol. 69 , p. 125 .

SAML ( 2019 ), “ SAML ”, available at: http://saml.xml.org (accessed August 2019 ).

Sethi , M. , Arkko , J. and Back , H.M. ( 2018 ), “ Practical considerations and implementation experiences in securing smart object networks ”, IETF RFC , available at: https://tools.ietf.org/html/rfc8387 (accessed August 2019 ).

Sharma , S. , Tripathi , M.M. and Mishra , V.M. ( 2017 ), “ Survey paper on sensors for body area network in health care ”, The IEEE International Conference in Emerging Trends in Computing and Communication Technologies (ICETCCT) .

Shibboleth ( 2019 ), available at. www.internet2.edu/products-services/trust-identity/shibboleth/ Accessed: August 2019.

Shodan ( 2019 ), available at: www.shodan.io/ (accessed August 2019 ).

Singh , V.P. , Dwarakanath , V.T. , Haribabu , P. and Babu , N.S.C. ( 2017 ), “ IoT standardization efforts — an analysis ”, IEEE International Conference On Smart Technologies For Smart Nation (SmartTechCon) , Bangalore .

Sonune , S. , Kalbande , D. , Yeole , A. and Oak , S. ( 2017 ), “ Issues in IoT healthcare platforms: a critical study and review ”, IEEE International Conference on Intelligent Computing and Control (I2C2) , Coimbatore .

Strauss , A.T. , Martinez , D.A. , Garcia-Arce , A. , Taylor , S. , Mateja , C. , Fabri , P.J. and Zayas-Castro , J.L. ( 2015 ), “ A user needs assessment to inform health information exchange design and implementation ”, BMC Medical Informatics and Decision Making , Vol. 15 No. 1 , pp. 1 - 11 , available at: http://dx.doi.org/10.1186/s12911-015-0207-x

Stuurman , K. and Kamara , I. ( 2016 ), “ IoT standardization – the approach in the field of data protection as a model for ensuring compliance of IoT applications? ”, 4th IEEE International Conference on Future Internet of Things and Cloud Workshops , Vienna .

Suyts , V.P. , Shadrin , A.S. and Leonov , P.Y. ( 2017 ), “ The analysis of big data and the accuracy of financial reports ”, 5th International Conference on Future Internet of Things and Cloud Workshops , Prague .

Szerejko , J.D. ( 2015 ), “ Reading between the lines of electronic health records: the health information technology for economic and clinical health act and its implications for health care fraud and information security ”, Connecticut Law Review , Vol. 47 No. 4 .

Tse , D. , Chow , C.K. , Ly , T.P. , Tong , C.Y. and Tam , K.W. ( 2018 ), “ The challenges of big data governance in healthcare ”, 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications , New York, NY .

Uddin , M.S. , Alam , J.B. and Banu , S. ( 2017 ), “ Real time patient monitoring system based on internet of things ”, 4th IEEE International Conference on Advances in Electrical Engineering , Dhaka .

US DHHS ( 2019 ), “ US department of health and human services, breach portal: notice to the secretary of HHS breach of unsecured protected health information ”, available at: https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf (accessed August 2019 ).

WHO ( 2018 ), “ Global health and aging ”, World Health Organization, 2011 , available at: www.who.int/ (accessed August 2018 ).

Williams , C. , Mostashari , F. , Mertz , K. , Hogin , E. and Atwal , P. ( 2012 ), “ From the office of the national coordinator: the strategy for advancing the exchange of health information ”, Health Affairs , Vol. 31 No. 3 , pp. 527 - 536 , available at: http://content.healthaffairs.org/content/31/3/527.abstract

Williams , P.A. and McCauley , V. ( 2016 ), “ Always connected: the security challenges of the healthcare internet of things ”, 3rd IEEE World Forum on the Internet of Things , Reston, VA .

Yang , Z. , Zhou , Q. , Lei , L. , Zheng , K. and Xiang , W. ( 2016 ), “ An IoT-cloud based wearable ECG monitoring system for smart healthcare ”, Journal of Medical Systems , Vol. 40 No. 12 , p. 286 .

Yuehong , Y.I.N. , Zeng , Y. , Chen , X. and Fan , Y. ( 2016 ), “ The internet of things in healthcare: an overview ”, Journal of Industrial Information Integration , Vol. 1 , pp. 3 - 13 .

Zagan , I. , Gaitan , V.G. , Petrariu , A.I. and Brezulianu , A. ( 2017 ), “ Healthcare IoT m-green CARDIO remote cardiac monitoring system – concept, theory of operation and implementation ”, Advances in Electrical and Computer Engineering Journal , Vol. 17 No. 2 , pp. 23 - 31 .

Zeadally , S. and Bello , O. ( 2019 ), “ Harnessing the power of internet of things based connectivity to improve healthcare ”, Internet of Things , p. 100074 , available at: doi.org/10.1016/j.iot.2019.100074

Zeadally , S. , Isaac , J.T. and Baig , Z. ( 2016 ), “ Security attacks and solutions in electronic health (E-health) systems ”, Journal of Medical Systems , Vol. 40 No. 12 , p. 263 .

Zilani , K.A. , Yeasmin , R. , Zubair , K.A. , Sammir , M.R. and Sabrin , S. ( 2018 ), “ R 3 HMS, an IoT based approach for patient health monitoring ”, IEEE International Conference on Computer, Communication, Chemical, Material and Electronic Engineering , Rajshahi .

Further reading

Cloudera ( 2019 ), “ Platfora ”, available at: www.cloudera.com/partners/solutions/platfora.html (accessed August 2019 ).

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• Published: 08 October 2020

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis

• Xuejie Yang 1   na1 ,
• Xiaoyu Wang   ORCID: orcid.org/0000-0003-2085-2924 2   na1 ,
• Xingguo Li 1   na1 ,
• Dongxiao Gu 1 , 3   na1 ,
• Changyong Liang 1 ,
• Kang Li 1 ,
• Gongrang Zhang 1 &
• Jinhong Zhong 1  

BMC Medical Informatics and Decision Making volume  20 , Article number:  260 ( 2020 ) Cite this article

12k Accesses

39 Citations

Metrics details

At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas.

We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003–2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis.

The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research.

Conclusions

This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

Peer Review reports

The Internet of Things (IoT) is an important representative of the new generation of information technology. It is the result of rapid development in the field of wireless communications in recent years, and it is a network that extends on the Internet [ 1 ]. It can connect various information sensing devices (such as Radio Frequency Identification, infrared sensors, laser scanners, etc.) to the Internet to realize the “Internet of Everything” [ 2 ]. At present, IoT has been widely used in various fields, such as smart city, smart home, intelligent logistics, intelligent transportation, etc. Among them, smart health is also one of its important application areas. There are countless people who lose their lives every year due to various diseases or health problems. In terms of chronic diseases, the number of people dying from chronic diseases accounts for 60% of the total number of deaths worldwide. People are paying more and more attention to health issues [ 3 ]. Therefore, the use of IoT technology to solve health problems has become one of the research hotspots in the field of smart health.

IoT is connecting physical world with virtual world of Internet. Physical world includes household appliances (such as air purifiers, thermostats, etc.), automobiles, industrial machinery, construction, medical equipment, and human body [ 4 ]. Applying IoT technologies to healthcare will help improve the quality of people life, the level of chronic disease management, danger warning and life-saving interventions. There are lots potential applications of healthcare IoT: (1) Health monitoring . Today’s wearable devices can detect basic activities of human body, analyze human behavior, and measure health status. Smart wearable devices (such as smart watch) can reduce patient anxiety and reduce waste of resources [ 5 ]. This is very different from other sensitive devices for health monitoring in conventional hospitals. (2) Health information support for patients . You can remind patients to take medicine on time through some IoT devices, in clinical. Networking devices such as electrocardiogram, blood oxygen, and blood pressure can improve the continuous measurement, monitoring, and support structure of patients and caregivers, thereby improving clinical outcomes [ 6 , 7 ]. (3) Service improvement . IoT can help connect cars to network systems. If a car has an accident, the system can identify the severity of the accident and help traffic administration department and healthcare emergency center via sending the accident location and direction. This will help the injured people obtain timely assistance [ 8 ]. (4) The collection of information resource for big data analytics . The health IoT can generate massive amounts of health big data. The analysis, mining, and use of health big data can further promote and enhance the development of health IoT [ 9 ].

Since 2003, scholars from all over the world have gradually invested in research in the field of smart health research based on the IoT. In response, some scholars have designed smart wearable systems to solve health problems. Li et al. [ 10 ] established a model of the acceptance of smart wearable system by the elderly, and pointed out the factors influencing their use of smart wearable technology, including self-reported health status. Akbulut et al. [ 11 ] designed a smart wearable system that monitors cardiovascular disease, which provides continuous medical monitoring. Fraise et al. [ 12 ] proposed a multi-agent system (MAS) that uses smart wearable and mobile technology to care for patients in elderly care facilities. In recent years, the development of technology has made smart watches and smart bracelets popular. Previously, Lu et al. [ 13 ] reviewed the application of smart watches in the field of medical health. Through comparative experiments, Hataji et al. [ 14 ] showed that combined treatment could improve the daily physical performance of patients with chronic obstructive pulmonary disease (COPD) under the encouragement of smart watches. Wile et al. [ 15 ] used smart watch devices to distinguish between orthostatic recurrent tremor and primary tremor of Parkinson’s disease. Grym et al. [ 16 ] pointed out that smart wristbands are a viable continuous monitoring tool during pregnancy. Smart home is also an important application of IoT technology in the field of smart health. Dawadi et al. [ 17 ] demonstrated the feasibility of using smart home sensor data and learning-based data analysis to predict clinical scores. Pham et al. [ 18 ] proposed a cloud-based smart home environment (CoSHE) for home healthcare. Ghasemi et al. [ 19 ] proposed a smart home medical system that can diagnose environmental events and health risks quickly and in a timely manner. Alberdi et al. [ 20 ] ‘s experiments show that all mobile, cognitive, and depressive symptoms can be predicted by activity-aware smart home data. In addition, research on disease and health issues through IoT technology is the focus of research in this field. Zhang et al. [ 21 ] proposed a medical data fusion algorithm based on IoT for the particularity of medical IoT data. Hossain et al. [ 22 ] proposed an industrial Internet of Things (IIoT) health monitoring framework that supports cloud computing. Farahani et al. [ 23 ] introduced the overall architecture of the fog-driven IoT e-health ecosystem and discussed the applicability and challenges of the IoT in the field of healthcare.

At present, there are many researches on the application of smart wearables, smart watches, smart bracelets, smart homes and IoT technologies to the field of smart health. However, there is no research to objectively review and visualize all the literature in this field. In order to analyze the development status and future trends of the intelligent health research field based on the IoT systematically, comprehensively, and objectively, this study uses bibliometric methods to visualize the analysis from time distribution, spatial distribution, literature co-citation and keywords based on 9561 literature data in this field from 2003 to 2019. This research provides panoramic knowledge support for researchers in related fields to understand the research status, future trends and hotspots in the field of smart health research based on IoT.

Data sources

The data source for this study was Web of Science (WoS), which selected four core databases of its core collections, including Science Citation Index Expanded, Conference Proceedings Citation Index-Science, and so on. WoS is an important database for obtaining global academic information. It contains more than 13,000 authoritative, high-impact academic journals from around the world, covering the fields of natural sciences, engineering technology, biomedicine, social sciences, arts and humanities. WoS includes references cited in the paper, with a unique citation index, users can use an article, a patent number, a conference document, a journal or a book as a search term to retrieve their citations and easily trace the origin and history of a research document, or track its latest progress. Although the WoS database cannot include all the literature published in this field, it has some representativeness. We invited 5 experts in the field of health IoT to finalize the database and search strategy through Delphi method. The search strategy we used is as follows: TS = (“#1” AND “#2”), Where “#1” is TS = (“internet of things” OR “smart watch*” OR “smart wristband*” OR “smart home*” OR “wearable device*” OR “wearable technolog*” OR “wearable sensor*”), indicating the search term related to the IoT; “#2” indicates TS = (“diseas*” OR “health*“OR “hospital*”), which indicates a search term related to health. In order to ensure that the retrieved documents are related to the retrieval subject, we organized a panel of evaluation consisting of eight Ph.D. candidates in our research area. After excluding irrelevant articles, we finally got 9561 records. (The search time was August 2020).

Research methods and tools

This paper mainly adopts the method of bibliometrics. Bibliometrics refers to the quantitative analysis and management of literature information by mathematical and statistical methods, and then discusses its structure, characteristics and laws [ 24 ]. This study mainly uses HistCite, CiteSpace and MS Excel to visually analyze the relevant literature in the field of smart health research based on IoT. Because HistCite’s statistical function is relatively powerful, it is mainly used to collect relevant data in this paper, and then use Excel software to draw the chart [ 25 ]. CiteSpace is a visualization tool for bibliometrics that focuses on finding key points in the development of a field [ 26 ]. Therefore, this article mainly uses CiteSpace to visualize the authors, institutions, literature co-citation and keywords of the IoT-based smart health research field.

Time distribution map

In order to understand the output of research results in the field of smart health research based on IoT, HistCite was used to statistically analyze the number of scientific literatures in the years from 2003 to 2019, and the trend of annual papers was obtained, as shown in Fig.  1 . As can be seen from the figure, from 2003 to 2019, the annual capacity curve presents an overall growth trend, among which the curve of annual capacity from 2003 to 2014 is relatively flat, and the annual capacity basically conforms to the exponential growth trend, or even below the exponential trend line. In 2014–2019, the annual capacity curve has grown very rapidly, almost showing a linear upward trend, much higher than the index trend line, with a growth rate of 97.88% in 2014–2015. This figure suggests that research in this field will continue to increase in the future and that it will remain a hotspot for future research.

Annual number of published articles

Then, we explored the input of researchers in the field of smart health research based on IoT. The same use of HistCite to statistically analyze the number of scientific research participants in the years from 2003 to 2019, and get the trend of the annual author input, as shown in Fig.  2 . By comparing Figs.  1 and 2 , we can clearly find that their variation trend is roughly the same, in years with a high growth rate of annual capacity, the growth rate of author input is also high. It is also easy to understand that, generally speaking, the annual number of articles and the annual amount of authors input is directly proportional to the relationship.

Annual number of authors input

Finally, the input-output ratio of scientific researchers in the field of smart health research based on IoT was understood. We calculated the number of participants in a single paper from 2003 to 2019, and obtained the change trend of the ratio of participants in a single paper, as shown in Fig.  3 . The straight line parallel to the abscissa in the figure is the average number of participants in a single document over the years, which is 3.79. Overall, the annual input-output ratio fluctuated significantly, especially during 2003–2011. This was because the number of documents in previous years was small and the researchers were not fixed. Then the ratio is roughly stable. In the 17 years, only the authors in 2010 and 2019 had an input-output ratio of more than 4. The small number of participants in a single document indicates that in this research field, the contribution rate of individual authors is high, the cooperation between authors is less, the number of authors is not saturated, and there is still much room for improvement.

Annual author input-output ratio

Space distribution map

Author distribution.

In order to analyze the author’s cooperative network, we import the preprocessed data into CiteSpace to generate the author cooperation network diagram, as shown in Fig.  4 . The figure shows the author’s name and the relationship between the authors with 10 or more articles. The most published is Bonato and Rahmani, which has published 35 articles. In the figure, the size of the node is proportional to the number of articles issued by the author, the thickness of the connection between the nodes is proportional to the number of cooperation between the authors, and different colors indicate the year of cooperation between different authors. As can be seen in the upper left corner of Fig.  4 , the number of network nodes is 811, the number of connections between nodes is 745, and the density of the network is only 0.0023.

Author collaboration network

Table  1 specifies the authors of the top 10 articles and their related information. In the HistCite software system, the citation frequency is divided into LCS and LGS, where LCS (local citation score) refers to the citation frequency of the document in the database of the field, and GCS (global citation score) refers to the frequency of citation in the Web of Science database [ 27 ]. It can be seen from Table 1 that Bonato and Rahmani have the largest number of publications, and the total quotation has reached 2024 and 908 respectively. The average single paper has been cited more than 15 times, indicating that his papers are not only issued more, but also have higher recognition. The sparseness of network density indicates that the cooperation between authors is not close enough, the field of smart health research based on IoT has not yet formed a stable core author group. And if the authors strengthen cooperation, they can collide with new sparks, promote research and innovation, and make the field flourish.

Institutional distribution

Then we import the pre-processed data into CiteSpace to analyze the organization that publishes the scientific literature, and generate the organization cooperation network diagram, as shown in Fig.  5 . Table  2 shows the top 10 organizations and related information. The organization with the largest number of publications is Chinese Academy of Sciences, which has published 134 scientific papers, 52 more than the second-ranked King Saud University. In addition, there are nearly 200 organizations that have published at least 10 articles, indicating that the research on the field of smart health research based on IoT has received extensive attention from various authoritative academic institutions in the world, showing a hundred schools of contention and a hundred flowers.

Institution collaboration network

According to Table 2 , Chinese Academy of Sciences not only has the largest number of publications, but its LCS and GCS are also much higher than the second-ranked King Saud University. Although Georgia Institute of Technology is ranked third in the number of publications, its LCS is not high. The average number of citations in the field is only 1.09, indicating that the published articles are not highly recognized by peers. On the contrary, although the numbers of published literatures are not large by Massachusetts Institute of Technology and Washington State University, their average total number of citations have reached 38 and 33 respectively. At the same time, Chinese Academy of Sciences’ LCS and GCS data ranks first among all institutions. These explain that the quality of the literature published by these institutions is very high and is widely supported and highly recognized by researchers and peers.

Research cooperation between institutions is an important way to enhance the research strength of the organization as a whole, to achieve complementary scientific research resources and to share knowledge, and to reflect one of the important indicators of research status in a certain field [ 28 ]. In Fig.  5 , the size of the node is proportional to the number of documents sent by the organization, the thickness of the connection between the nodes is proportional to the number of cooperative researches between the organizations, and different colors indicate the year of collaborative research between the institutions. The number of network nodes is 629, the number of connections between nodes is 881, and the density of the network is 0.0045. It can be seen that in the field of smart health research based on IoT, there is not enough cooperation between institutions, and the relationship between them is not close enough, and a stable and mature institutional cooperation relationship has not yet been formed. Institutions should strengthen cooperation and exchanges, give full play to their respective advantages, and make full use of academic resources to promote innovation in research results and promote the vigorous development of the entire field.

Journal distribution

Finally, we analyzed the journals in the field of smart health research based on IoT. We use HistCite to collect statistics on journals in the field, and Table  3 lists the top 10 journals and related information. Table 3 shows that the journal with the largest number of documents is Sensors , and its LCS and GCS data are also among the best. Interestingly, four of the top 10 journals are from the IEEE Publishing Group: IEEE Access , IEEE Internet of Things Journal , IEEE Sensors Journal and IEEE Journal of Biomedical and Health Informatics. The average single article of IEEE Journal of Biomedical and Health Informatics has been cited as high as 31.7, which is enough to see the journal’s influence in the field of smart health research based on IoT. Conversely, although there are many related articles in the journal of IEEE Access , its average times cited is only 14.9, indicating that the quality and influence of these articles are generally not too high.

Overall, IEEE is the biggest winner in the field of smart health research based on IoT. In addition, there are many journals that focus on the IoT, sensors and health, but there are few journals focusing on the intersection of the IoT and health. This shows that the research on the field of smart health research based on IoT has not yet had a great influence, and the major journals have not paid much attention to it.

Knowledge base analysis

Co-citation Networks refers to a knowledge network formed by two scientific documents simultaneously cited by the third article [ 29 ]. Literature co-citation analysis expresses the relationship between documents by the frequency cited by other literatures. That is to say, a certain two documents are cited together by several other documents. The higher the frequency of citations, the closer the relationship between the two documents is, which means that the more the subject backgrounds of the two documents are similar [ 30 ]. Fundamentally speaking, when certain documents, journals, academic groups or individuals are repeatedly quoted by their peers, the knowledge carriers that are cited are essentially recognized by the scientific community in which they are located, thus forming a scientific paradigm. This paradigm relationship can be visualized by analysis of the co-citation network of the literature [ 31 ]. Therefore, through the literature co-citation network analysis, the knowledge base of the research on the field of smart health research based on IoT can be concretely demonstrated.

We import the preprocessed data into CiteSpace, analyze the co-citation relationship between scientific literatures, and generate a co-citation network diagram of the literature, as shown in Fig.  6 . In the figure, each node represents a document that is commonly cited. The size of the node is proportional to the number of times it is cited, the connection between the nodes indicates a co-citation relationship. The thickness of the connection indicates the strength of the co-cited, and the different colors indicate the year in which the document was cited. The number of network nodes is 1239, the number of connections between nodes is 2331, and the density of the network is 0.003.

Articles in the co-citation network

Table  4 lists the top 10 co-citation literature and related information. In the literature citation network, Atzori’s article published in Computer Networks in 2010 titled “The internet of things: A survey” was cited as the highest frequency, reaching 290 times [ 1 ]. So far, this article has been cited as a total of 13,691 times in Google Scholar. Gubbi [ 32 ], Islam [ 33 ], Patel [ 34 ], Al-Fuqaha [ 35 ], etc., which are ranked behind, are connected to Atzori [ 1 ]. It shows that the correlation between the top citations in the field is very strong, and the topics of the scientific literature are similar. These documents are all about IoT technology and applications. It can be seen that the current research on IoT technology has been initially matured and standardized. Interestingly, five of the top 10 cited articles were published by the IEEE Publishing Group.

Combined with the above-mentioned journal analysis, it can be said that the IEEE Publishing Group has made tremendous contributions to the development of this field. In addition, in terms of centrality, Atzori’s performance is also very good, the greater the centrality of a node in the network, indicating that it is more important in the network [ 36 ]. Therefore, the comprehensiveness of all aspects can reflect the importance of the document Atzori [ 1 ]. It can be said that this article lays the foundation for the research of smart health research based on IoT. In general, the literature of Fig.  6 has a relatively tightly distributed network, indicating that the knowledge base in this field has been initially formed, which will provide important knowledge support for subsequent research.

Research focus analysis

Research hotspots refer to the focus of research in a certain discipline in a certain period of time. Generally speaking, there is a large number of scientific literatures, academic thoughts, and research groups emerging on a subject [ 37 ]. Kuhn [ 38 ] emphasized that the development of science is an alternating appearance of the conventional science and the scientific revolution, which indicates that the scientific revolution is changing, and that the old and new paradigms are incommensurable. It is precisely because of the existence of incommensurability that the vocabulary system between the old and new paradigms will change accordingly. So, whether the scientific revolution occurs can be judged from whether the vocabulary of the period has changed. The number of co-occurrences of different keywords in the scientific literature can be counted. The level of the co-occurrence frequency can reflect the correlation between the keywords and the hot issues in the specific field during this period [ 39 ]. Therefore, the co-occurrence analysis of keywords can reveal the research structure and research hotspots in specific fields. The research results of Callon et al. [ 40 ] are the earliest applications of co-word analysis. Subsequently, the co-word analysis method has been widely used in the field of information science. Of course, keyword analysis is also based on certain assumptions [ 41 ], including: (1) The selection of keywords is cautious; (2) Multiple keywords in the same document are related to each other and are recognized by the author; (3) If enough authors recognize the relationship between the same keyword, then this relationship can be considered to have a certain meaning in the field; (4) The keyword can reflect the content of the document to a certain extent. When the author chooses the keyword, it is usually affected by other research results. The basic principle of co-word analysis is to count the number of times a group of keywords appears in the same group of documents, then the degree of co-occurrence is measured by the number of co-occurrences, the more co-occurrences, the more closely they are related [ 42 ].

The key word is a high degree of conciseness and generalization of an article, which is the core and essence of the article, frequently high keywords are often used to identify hot topics in a research field. By analyzing keywords, we can intuitively grasp the main research content of a paper, and even the overall research situation in a field [ 43 ]. This study extracts the key words from 9561 documents and conducts frequency statistics and frequency co-occurrence analysis to understand the current structural foundations and research hotspots in the field of smart health research based on IoT, and predict the future development direction of the field. Table  5 lists the top 20 keywords of the co-occurrence frequency. It can be seen that the keyword with the highest frequency is the internet of things, this is very consistent with the topic of this article. The subject of this paper is the IoT and health. The IoT is also the keyword with the highest centrality, indicating that research in this field is basically carried out around the IoT. These keywords with high frequency of occurrence can be divided into four main categories: (1) Keywords related to the IoT technology, such as internet of thing, sensor, wireless sensor network, big data, cloud computing, etc.; (2) Keywords related to health, such as healthcare, health, parkinsons disease, etc.; (3) Keywords related to smart health, such as smart home; (4) Problems arising from research in the field of smart health research based on IoT, such as security.

The co-word network refers to an objective knowledge network that expresses the structure of the scientific knowledge domain, which is composed of co-occurrence between keywords. It can be used to describe the knowledge structure of a subject domain and can reveal the evolution of a disciplinary structure in combination with time series [ 44 ]. We import the pre-processed data into CiteSpace to analyze the keywords of the scientific literature and generate a keyword co-occurrence network diagram, as shown in Fig.  7 . Each node represents a different keyword, and the size of the node is proportional to the frequency of its co-occurrence. The connection between nodes indicates the co-occurrence relationship between two keywords in the same document, and the different colors indicate the years in which different keywords co-occur. The number of network nodes is 889, the number of connections between nodes is 3090, and the density of the network is 0.0078. It can be seen from the figure that the network as a whole is relatively dense, and the co-occurrence relationship between multiple groups of keywords is relatively close, indicating that the research results in the field of intelligent health research based on the Internet of Things are mostly multi-theme research.

Keyword co-occurrence network

In the future research, the IoT is still the center of research in this field. Focusing on the integration of the IoT and the integration of smart health, it is necessary to solve the security and privacy issues in this field and eliminate concerns for users. IoT technologies often appear at the same time as cloud computing, big data, etc. These emerging technologies are closely related, and artificial intelligence technology cannot lack them. Therefore, in future research, the IoT, big data and cloud computing will frequently appear in the research in this field. Smart homes and smart cities have received a lot of attention in recent years. Smart home is a platform for housing, using IoT technology to connect various devices in the home to achieve convenience, comfort and intelligence. At present, there are few studies on the use of IoT technology to treat specific diseases (such as parkinsons diseases appearing in the top 20 keywords, etc.), so this may be one of the future research trends.

Discussions

Summary of findings.

Using HistCite, CiteSpace, Excel and other analysis tools, the time distribution, spatial distribution, literature citation and research hotspots of knowledge in this field are deeply analyzed and visualized.

(1) In terms of time distribution: the annual load capacity curve and the annual author input curve change trend are roughly the same, and the overall growth trend. In particular, the growth rate since 2014 has been very rapid, almost showing a linear upward trend, much higher than the index trend line. Explain that research in this field will continue to increase in the future and that it will remain a hotspot for future research.

(2) In terms of spatial distribution: a) Author distribution: The author’s cooperation network is sparse, the cooperation between the authors is not close enough, and the stable core author group has not yet formed in this field; b) Institutional distribution: Similarly, the cooperation and cooperation between institutions is not close enough, and a stable and mature institutional cooperation relationship has not yet been formed; c) Journal distribution: There are few journals focusing on the intersection of IoT technology and health. This shows that the research in this field has not yet had a great influence, and the major journals have not paid much attention to it.

(3) In terms of knowledge base analysis: the literature has a relatively tightly indexed network, and the knowledge base in this field has been initially formed, which can provide important knowledge support for subsequent research.

(4) In terms of research hotspot analysis: high-frequency keywords can be divided into four parts. The Internet of Things is a keyword with the highest co-occurrence frequency and the highest centrality. In addition, most of the research results in this field are multi-theme research. Among them, smart home and the use of IoT technology to assist in the treatment of specific diseases are the future research trends.

Future trends

Through the research in this paper, we can find that smart home and smart city are the research hotspots in recent years, and will likely also be the focus of future research.

Smart homes can be a “family health consultant”. For example, the smart home system can realize the “alarm” of the elderly and children, notify the family and locate; The system will automatically start and shut down the air purifier according to the real-time air condition, without manual operation; In addition, the air purifier can be controlled based on the humidity level in the house and the PSI level of asthma and allergic rhinitis patients [ 45 ]. The smart screen in the kitchen can see the children in the living room through the security system, and is equipped with detection equipment for harmful gases such as gas to achieve the function of safety protection; The smart clothing care machine in the cloakroom has the functions of steam sterilization, shaping, drying and other functions to protect the health of users.

The construction of smart health protection system is an important part of the construction of smart cities. The construction of “digital health” system is the focus of promotion. The smart city will build a medical health big data platform, realize the data sharing and sharing of medical and health service organizations, and rely on the smart city cloud platform to form a citizen medical health information big data center, and provide support for promoting three-medicine linkage and achieving graded diagnosis and treatment. In addition, electronic health records of residents in the city will be established to realize the networking of health services in hospitals and clinics throughout the city. And promote remote registration, electronic toll collection, online telemedicine services, graphic and physical examination diagnostic systems, etc., to comprehensively improve the city’s medical and health services.

Future works

The following work will be done in the future studies: (1) The first one it to examine how the Internet of Things actually affects medical accessibility; (2) The second one is to evaluate the objectiveness or reproducibility of reported results as well as relationship between prominent authors and industry; (3) The third one is to use more data resources rather than only WoS data and further validate our search result; (4) The fourth but not the last one is to eliminate irrelevant search results with technical tools rather than the manual methods used on this study.

In order to explore the knowledge base, research hotspots, development status and future research directions of the research on the field of smart health research based on IoT. We conducted a bibliometric analysis of 9561 articles in the Web of Science core database for the 17 years from 2003 to 2019. The results show that the field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. Our research provides panoramic knowledge support for researchers in related fields to understand the research status, future trends and hotspots in the field of smart health research based on IoT.

Availability of data and materials

Data materials are available from the lead author upon request.

Abbreviations

• Internet of things

Multi-agent system

Chronic obstructive pulmonary disease

Cloud-based smart home environment

Industrial Internet of Things

Web of Science

Local citation score

Global citation score

Atzori L, Iera A, Morabito G. The internet of things: a survey. Comput Netw. 2010;54(15):2787–805.

Article   Google Scholar  

Stankovic JA. Research directions for the internet of things. IEEE Internet Things J. 2014;1(1):3–9.

Zhang G, Li K, Gu D, Wang X, Yang X, Zhu K, Liang G. Visualizing knowledge evolution and hotspots of rural environment and health: a systematic review and research direction. IEEE Access. 2019;7:72538–50.

Greengard S. The internet of things: MIT press; 2015.

Book   Google Scholar  

Abawajy JH, Hassan MM. Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Commun Mag. 2017;55(1):48–53.

Fernandes CO, De Lucena CJP. A software framework for remote patient monitoring by using multi-agent systems support. JMIR Med Inform. 2017;5(1):e9.

Article   PubMed   PubMed Central   Google Scholar  

Liang C, Gu D, Tao F, et al. Influence of mechanism of patient-accessible hospital information system implementation on doctor–patient relationships: a service fairness perspective. Inf Manag. 2017;54(1):57–72.

Thomas MO, Rad BB. Reliability evaluation metrics for internet of things, car tracking system: a review. Int J Inf Technol Comput Sci (IJITCS). 2017;9(2):1–10.

CAS   Google Scholar  

Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP. Machine learning for internet of things data analysis: a survey. Digit Commun Netw. 2018;4(3):161–75.

Li J, Ma Q, Chan AH, Man S. Health monitoring through wearable technologies for older adults: smart wearables acceptance model. Appl Ergon. 2019;75:162–9.

Article   PubMed   Google Scholar  

Akbulut FP, Akan A. A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics. Measurement. 2018;128:237–46.

Fraile JA, Zato C, Gil O, de la Prieta F. Multiagent Systems and Wearable Devices: Helping People Live Healthier. In: Augusto JC, Corchado JM, Novais P, Analide C, editors. Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010). Advances in Soft Computing, vol 72. Berlin, Heidelberg: Springer; 2010.. https://doi.org/10.1007/978-3-642-13268-1_2 .

Lu T-C, Fu C-M, Ma MH-M, Fang C-C, Turner AM. Healthcare applications of smart watches. Appl Clin Inform. 2016;7(03):850–69.

Hataji O, Nishii Y, Ito K, Sakaguchi T, Saiki H, Suzuki Y, D'Alessandro-Gabazza C, Fujimoto H, Kobayashi T, Gabazza EC. Smart watch-based coaching with tiotropium and olodaterol ameliorates physical activity in patients with chronic obstructive pulmonary disease. Exp Therapeutic Med. 2017;14(5):4061–4.

Wile DJ, Ranawaya R, Kiss ZH. Smart watch accelerometry for analysis and diagnosis of tremor. J Neurosci Methods. 2014;230:1–4.

Grym K, Niela-Vilén H, Ekholm E, Hamari L, Azimi I, Rahmani A, Liljeberg P, Löyttyniemi E, Axelin A. Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth. BMC Pregnancy Childbirth. 2019;19(1):34.

Dawadi PN, Cook DJ, Schmitter-Edgecombe M. Automated cognitive health assessment from smart home-based behavior data. IEEE J Biomed Health Inform. 2015;20(4):1188–94.

Pham M, Mengistu Y, Do H, Sheng W. Delivering home healthcare through a cloud-based smart home environment (CoSHE). Futur Gener Comput Syst. 2018;81:129–40.

Ghasemi F, Rezaee A, Rahmani AM. Structural and behavioral reference model for IoT-based elderly health-care systems in smart home. Int J Commun Syst. 2019;32(12):e4002.

Alberdi A, Weakley A, Schmitter-Edgecombe M, Cook DJ, Aztiria A, Basarab A, Barrenechea M. Smart home-based prediction of multidomain symptoms related to Alzheimer's disease. IEEE J Biomed Health Inform. 2018;22(6):1720–31.

Zhang W, Yang J, Su H, Kumar M, Mao Y. Medical data fusion algorithm based on internet of things. Pers Ubiquit Comput. 2018;22(5–6):895–902.

Hossain MS, Muhammad G. Cloud-assisted industrial internet of things (iiot)–enabled framework for health monitoring. Comput Netw. 2016;101:192–202.

Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K. Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Futur Gener Comput Syst. 2018;78:659–76.

Qiu J-P, Dong K, Yu H-Q. Comparative study on structure and correlation among author co-occurrence networks in bibliometrics. Scientometrics. 2014;101(2):1345–60.

Garfield E. From the science of science to Scientometrics visualizing the history of science with HistCite software. J Inf Secur. 2009;3(3):173–9.

Google Scholar  

Chen C. Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci. 2004;101(suppl 1):5303–10.

Article   CAS   PubMed   Google Scholar  

Emami Z, Hariri N, Khamseh ME, Nooshinfard F. Mapping diabetes research in middle eastern countries during 2007-2013: a scientometric analysis. Med J Islam Repub Iran. 2018;32:84.

PubMed   PubMed Central   Google Scholar  

Gu D, Li J, Li X, Liang C. Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inform. 2017;98:22–32.

Leung XY, Sun J, Bai B. Bibliometrics of social media research: a co-citation and co-word analysis. Int J Hosp Manag. 2017;66:35–45.

Holman D, Lynch R, Reeves A. How do health behaviour interventions take account of social context? A literature trend and co-citation analysis. Health. 2018;22(4):389–410.

Chen C. Science mapping: a systematic review of the literature. J Data Inf Sci. 2017;2(2):1–40.

Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst. 2013;29(7):1645–60.

Islam SMR, Kwak D, Kabir MH, Hossain M, Kwak K. The internet of things for health care: a comprehensive survey. IEEE Access. 2015;3:678–708.

Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J NeuroEng Rehabil. 2012;9(1):21.

Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutorials. 2015;17(4):2347–76.

Gu D, Li K, Wang X, Liang C. Visualizing Knowledge Evolution of Emerging Information Technologies in Chronic Diseases Research. In: Chen H, Fang Q, Zeng D, Wu J, editors. Smart Health. ICSH 2018, Wuhan, China. Lecture Notes in Computer Science, vol 10983. Cham: Springer; 2018. https://doi.org/10.1007/978-3-030-03649-2_26 .

Gu D, Li T, Wang X, Yang X, Yu Z. Visualizing the intellectual structure and evolution of electronic health and telemedicine research. Int J Med Inform. 2019;130:103947.

Kuhn Thomas. The Structure of Scientific Revolutions[J]. Philos Books. 1962;4(3):14–16.

Gu D, Li K, Wang X, Li X, Liu F, Jiang L, Zhang F. Discovering and visualizing knowledge evolution of chronic disease research driven by emerging technologies. IEEE Access. 2019;7:72994–3003.

Callon M, Courtial J-P, Turner WA, Bauin S. From translations to problematic networks: an introduction to co-word analysis. Information (International Social Science Council). 1983;22(2):191–235.

Whittaker J. Creativity and conformity in science: titles, keywords and co-word analysis. Soc Stud Sci. 1989;19(3):473–96.

Yan B-N, Lee T-S, Lee T-P. Mapping the intellectual structure of the internet of things (IoT) field (2000–2014): a co-word analysis. Scientometrics. 2015;105(2):1285–300.

Apriliyanti ID, Alon I. Bibliometric analysis of absorptive capacity. Int Bus Rev. 2017;26(5):896–907.

Zhao W, Mao J, Lu K. Ranking themes on co-word networks: exploring the relationships among different metrics. Inf Process Manag. 2018;54(2):203–18.

Gu D, Deng S, Zheng Q, et al. Impacts of case-based health knowledge system in hospital management: the mediating role of group effectiveness. Inf Manag. 2019;56(8):103162.

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Acknowledgements

We would like to thank Fenghong Liu and Sofiya Shaanova for their insightful comments on an earlier version of this manuscript.

The dataset collection and analysis of this research were partially supported by the National Natural Science Foundation of China (NSFC) under grant Nos. 71771077, 71771075 and 72071063, and Fundamental Research Funds for the central universities under grant No. PA2020GDKC0020.

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Xuejie Yang, Xiaoyu Wang, Xingguo Li and Dongxiao Gu contributed equally to this work.

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The School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, Anhui, China

Xuejie Yang, Xingguo Li, Dongxiao Gu, Changyong Liang, Kang Li, Gongrang Zhang & Jinhong Zhong

The 1st Affiliated Hospital, Anhui University of Traditional Chinese Medicine, 117 Meishan Road, Hefei, 230031, Anhui, China

Xiaoyu Wang

Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei, 230009, Anhui, China

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XJY, XYW and DXG contributed to writing the manuscript. KL and XJY collected, analyzed and interpreted the data. CYL, XGL, GRZ and JHZ revised and improved the manuscript. All authors read and approved the final manuscript.

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Yang, X., Wang, X., Li, X. et al. Exploring emerging IoT technologies in smart health research: a knowledge graph analysis. BMC Med Inform Decis Mak 20 , 260 (2020). https://doi.org/10.1186/s12911-020-01278-9

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Healthcare predictive analytics using machine learning and deep learning techniques: a survey

• Mohammed Badawy   ORCID: orcid.org/0000-0001-9494-1386 1 ,
• Nagy Ramadan 1 &
• Hesham Ahmed Hefny 2  

Journal of Electrical Systems and Information Technology volume  10 , Article number:  40 ( 2023 ) Cite this article

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Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.

Introduction

Each day, human existence evolves, yet the health of each generation either improves or deteriorates. There are always uncertainties in life. Occasionally encounter many individuals with fatal health problems due to the late detection of diseases. Concerning the adult population, chronic liver disease would affect more than 50 million individuals worldwide. However, if the sickness is diagnosed early, it can be stopped. Disease prediction based on machine learning can be utilized to identify common diseases at an earlier stage. Currently, health is a secondary concern, which has led to numerous problems. Many patients cannot afford to see a doctor, and others are extremely busy and on a tight schedule, yet ignoring recurring symptoms for an extended length of time can have significant health repercussions [ 1 ].

Diseases are a global issue; thus, medical specialists and researchers are exerting their utmost efforts to reduce disease-related mortality. In recent years, predictive analytic models has played a pivotal role in the medical profession because of the increasing volume of healthcare data from a wide range of disparate and incompatible data sources. Nonetheless, processing, storing, and analyzing the massive amount of historical data and the constant inflow of streaming data created by healthcare services has become an unprecedented challenge utilizing traditional database storage [ 2 , 3 , 4 ]. A medical diagnosis is a form of problem-solving and a crucial and significant issue in the real world. Illness diagnosis is the process of translating observational evidence into disease names. The evidence comprises data received from evaluating a patient and substances generated from the patient; illnesses are conceptual medical entities that detect anomalies in the observed evidence [ 5 ].

Healthcare is the collective effort of society to ensure, provide, finance, and promote health. In the twentieth century, there was a significant shift toward the ideal of wellness and the prevention of sickness and incapacity. The delivery of healthcare services entails organized public or private efforts to aid persons in regaining health and preventing disease and impairment [ 6 ]. Health care can be described as standardized rules that help evaluate actions or situations that affect decision-making [ 7 ]. Healthcare is a multi-dimensional system. The basic goal of health care is to diagnose and treat illnesses or disabilities. A healthcare system’s key components are health experts (physicians or nurses), health facilities (clinics and hospitals that provide medications and other diagnostic services), and a funding institution to support the first two [ 8 ].

With the introduction of systems based on computers, the digitalization of all medical records and the evaluation of clinical data in healthcare systems have become widespread routine practices. The phrase "electronic health records" was chosen by the Institute of Medicine, a division of the National Academies of Sciences, Engineering, and Medicine, in 2003 to define the records that continued to enhance the healthcare sector for the benefit of both patients and physicians. Electronic Health Records (EHR) are "computerized medical records for patients that include all information in an individual's past, present, or future that occurs in an electronic system used to capture, store, retrieve, and link data primarily to offer healthcare and health-related services," according to Murphy, Hanken, and Waters [ 8 ].

Daily, healthcare services produce an enormous amount of data, making it increasingly complicated to analyze and handle it in "conventional ways." Using machine learning and deep learning, this data may be properly analyzed to generate actionable insights. In addition, genomics, medical data, social media data, environmental data, and other data sources can be used to supplement healthcare data. Figure  1 provides a visual picture of these data sources. The four key healthcare applications that can benefit from machine learning are prognosis, diagnosis, therapy, and clinical workflow, as outlined in the following section [ 9 ].

Illustration of heterogeneous sources contributing to healthcare data [ 9 ]

The long-term investment in developing novel technologies based on machine learning as well as deep learning techniques to improve the health of individuals via the prediction of future events reflects the increased interest in predictive analytics techniques to enhance healthcare. Clinical predictive models, as they have been formerly referred to, assisted in the diagnosis of people with an increased probability of disease. These prediction algorithms are utilized to make clinical treatment decisions and counsel patients based on some patient characteristics [ 10 ].

The concept of medical care is used to stress the organization and administration of curative care, which is a subset of health care. The ecology of medical care was first introduced by White in 1961. White also proposed a framework for perceiving patterns of health concerning symptoms experienced by populations of interest, along with individuals’ choices in getting medical treatment. In this framework, it is possible to calculate the proportion of the population that used medical services over a specific period of time. The "ecology of medical care" theory has become widely accepted in academic circles over the past few decades [ 6 ].

Medical personnel usually face new problems, changing tasks, and frequent interruptions because of the system's dynamism and scalability. This variability often makes disease recognition a secondary concern for medical experts. Moreover, the clinical interpretation of medical data is a challenging task from an epistemological point of view. This not only applies to professionals with extensive experience but also to representatives, such as young physician assistants, with varied or little experience [ 11 ]. The limited time available to medical personnel, the speedy progression of diseases, and the fluctuating patient dynamics make diagnosis a particularly complex process. However, a precise method of diagnosis is critical to ensuring speedy treatment and, thus, patient safety [ 12 ].

Predictive analytics for health care are critical industry requirements. It can have a significant impact on the accuracy of disease prediction, which can save patients' lives in the case of an accurate and timely prediction but can also endanger patients' lives in the case of an incorrect prediction. Diseases must therefore be accurately predicted and estimated. As a result, dependable and efficient methods for healthcare predictive analysis are required.

The purpose of this paper is to present a comprehensive review of common machine learning and deep learning techniques that are utilized in healthcare prediction, in addition to identifying the inherent obstacles that are associated with applying these approaches in the healthcare domain.

The rest of the paper is organized as follows: Section  " Background " gives a theoretical background on artificial intelligence, machine learning, and deep learning techniques. Section  " Disease prediction with analytics " outlines the survey methodology and presents a literature review of machine learning as well as deep learning approaches employed in healthcare prediction. Section  " Results and Discussion " gives a discussion of the results of previous works related to healthcare prediction. Section  " Challenges " covers the existing challenges related to the topic of this survey. Finally, Section  " Conclusion " concludes the paper.

The extensive research and development of cutting-edge tools based on machine learning and deep learning for predicting individual health outcomes demonstrate the increased interest in predictive analytics techniques to improve health care. Clinical predictive models assisted physicians in better identifying and treating patients who were at a higher risk of developing a serious illness. Based on a variety of factors unique to each individual patient, these prediction algorithms are used to advise patients and guide clinical practice.

Artificial intelligence (AI) is the ability of a system to interpret data, and it makes use of computers and machines to improve humans' capacity for decision-making, problem-solving, and technological innovation [ 13 ]. Figure  2 depicts machine learning and deep learning as subsets of AI.

AI, ML, and DL

Machine learning

Machine learning (ML) is a subfield of AI that aims to develop predictive algorithms based on the idea that machines should have the capability to access data and learn on their own [ 14 ]. ML utilizes algorithms, methods, and processes to detect basic correlations within data and create descriptive and predictive tools that process those correlations. ML is usually associated with data mining, pattern recognition, and deep learning. Although there are no clear boundaries between these areas and they often overlap, it is generally accepted that deep learning is a relatively new subfield of ML that uses extensive computational algorithms and large amounts of data to define complex relationships within data. As shown in Fig.  3 , ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning [ 15 ].

Different types of machine learning algorithms

Supervised learning

Supervised learning is an ML model for investigating the input–output correlation information of a system depending on a given set of training examples that are paired between the inputs and the outputs [ 16 ]. The model is trained with a labeled dataset. It matches how a student learns fundamental math from a teacher. This kind of learning requires labeled data with predicted correct answers based on algorithm output [ 17 ]. The most widely used supervised learning-based techniques include linear regression, logistic regression, decision trees, random forests, support vector machines, K-nearest neighbor, and naive Bayes.

A. Linear regression

Linear regression is a statistical method commonly used in predictive investigations. It succeeds in forecasting the dependent, output, variable (Y) based on the independent, input, variable (X). The connection between X and Y is represented as shown in Eq.  1 assuming continuous, real, and numeric parameters.

where m indicates the slope and c indicates the intercept. According to Eq.  1 , the association between the independent parameters (X) and the dependent parameters (Y) can be inferred [ 18 ].

The advantage of linear regression is that it is straightforward to learn and easy to-eliminate overfitting through regularization. One drawback of linear regression is that it is not convenient when applied to nonlinear relationships. However, it is not recommended for most practical applications as it greatly simplifies real-world problems [ 19 ]. The implementation tools utilized in linear regression are Python, R, MATLAB, and Excel.

As shown in Fig.  4 , observations are highlighted in red, and random deviations' result (shown in green) from the basic relationship (shown in yellow) between the independent variable (x) and the dependent variable (y) [ 20 ].

Linear regression model

B. Logistic regression

Logistic regression, also known as the logistic model, investigates the correlation between many independent variables and a categorical dependent variable and calculates the probability of an event by fitting the data to a logistic curve [ 21 ]. Discrete mean values must be binary, i.e., have only two outcomes: true or false, 0 or 1, yes or no, or either superscript or subscript. In logistic regression, categorical variables need to be predicted and classification problems should be solved. Logistic regression can be implemented using various tools such as R, Python, Java, and MATLAB [ 18 ]. Logistic regression has many benefits; for example, it shows the linear relationship between dependent and independent variables with the best results. It is also simple to understand. On the other hand, it can only predict numerical output, is not relevant to nonlinear data, and is sensitive to outliers [ 22 ].

C. Decision tree

The decision tree (DT) is the supervised learning technique used for classification. It combines the values of attributes based on their order, either ascending or descending [ 23 ]. As a tree-based strategy, DT defines each path starting from the root using a data-separating sequence until a Boolean conclusion is attained at the leaf node [ 24 , 25 ]. DT is a hierarchical representation of knowledge interactions that contains nodes and links. When relations are employed to classify, nodes reflect purposes [ 26 , 27 ]. An example of DT is presented in Fig.  5 .

Example of a DT

DTs have various drawbacks, such as increased complexity with increasing nomenclature, small modifications that may lead to a different architecture, and more processing time to train data [ 18 ]. The implementation tools used in DT are Python (Scikit-Learn), RStudio, Orange, KNIME, and Weka [ 22 ].

D. Random forest

Random forest (RF) is a basic technique that produces correct results most of the time. It may be utilized for classification and regression. The program produces an ensemble of DTs and blends them [ 28 ].

In the RF classifier, the higher the number of trees in the forest, the more accurate the results. So, the RF has generated a collection of DTs called the forest and combined them to achieve more accurate prediction results. In RF, each DT is built only on a part of the given dataset and trained on approximations. The RF brings together several DTs to reach the optimal decision [ 18 ].

As indicated in Fig.  6 , RF randomly selects a subset of features from the data, and from each subset it generates n random trees [ 20 ]. RF will combine the results from all DTs and provide them in the final output.

Random forest architecture

Two parameters are being used for tuning RF models: mtry —the count of randomly selected features to be considered in each division; and ntree —the model trees count. The mtry parameter has a trade-off: Large values raise the correlation between trees, but enhance the per-tree accuracy [ 29 ].

The RF works with a labeled dataset to do predictions and build a model. The final model is utilized to classify unlabeled data. The model integrates the concept of bagging with a random selection of traits to build variance-controlled DTs [ 30 ].

RF offers significant benefits. First, it can be utilized for determining the relevance of the variables in a regression and classification task [ 31 , 32 ]. This relevance is measured on a scale, based on the impurity drop at each node used for data segmentation [ 33 ]. Second, it automates missing values contained in the data and resolves the overfitting problem of DT. Finally, RF can efficiently handle huge datasets. On the other side, RF suffers from drawbacks; for example, it needs more computing and resources to generate the output results and it requires training effort due to the multiple DTs involved in it. The implementation tools used in RF are Python Scikit-Learn and R [ 18 ].

E. Support vector machine

The supervised ML technique for classification issues and regression models is called the support vector machine (SVM). SVM is a linear model that offers solutions to issues that are both linear and nonlinear. as shown in Fig.  7 . Its foundation is the idea of margin calculation. The dataset is divided into several groups to build relations between them [ 18 ].

Support vector machine

SVM is a statistics-based learning method that follows the principle of structural risk minimization and aims to locate decision bounds, also known as hyperplanes, that can optimally separate classes by finding a hyperplane in a usable N-dimensional space that explicitly classifies data points [ 34 , 35 , 36 ]. SVM indicates the decision boundary between two classes by defining the value of each data point, in particular the support vector points placed on the boundary between the respective classes [ 37 ].

SVM has several advantages; for example, it works perfectly with both semi-structured and unstructured data. The kernel trick is a strong point of SVM. Moreover, it can handle any complex problem with the right functionality and can also handle high-dimensional data. Furthermore, SVM generalization has less allocation risk. On the other hand, SVM has many downsides. The model training time is increased on a large dataset. Choosing the right kernel function is also a difficult process. In addition, it is not working well with noisy data. Implementation tools used in SVM include SVMlight with C, LibSVM with Python, MATLAB or Ruby, SAS, Kernlab, Scikit-Learn, and Weka [ 22 ].

F. K-nearest neighbor

K-nearest neighbor (KNN) is an "instance-based learning" or non-generalized learning algorithm, which is often known as a “lazy learning” algorithm [ 38 ]. KNN is used for solving classification problems. To anticipate the target label of the novel test data, KNN determines the distance of the nearest training data class labels with a new test data point in the existence of a K value, as shown in Fig.  8 . It then calculates the number of nearest data points using the K value and terminates the label of the new test data class. To determine the number of nearest-distance training data points, KNN usually sets the value of K according to (1): k  =  n ^(1/2), where n is the size of the dataset [ 22 ].

K-nearest neighbor

KNN has many benefits; for example, it is sufficiently powerful if the size of the training data is large. It is also simple and flexible, with attributes and distance functions. Moreover, it can handle multi-class datasets. KNN has many drawbacks, such as the difficulty of choosing the appropriate K value, it being very tedious to choose the distance function type for a particular dataset, and the computation cost being a little high due to the distance between all the training data points, the implementation tools used in KNN are Python (Scikit-Learn), WEKA, R, KNIME, and Orange [ 22 ].

G. Naive Bayes

Naive Bayes (NB) focuses on the probabilistic model of Bayes' theorem and is simple to set up as the complex recursive parameter estimation is basically none, making it suitable for huge datasets [ 39 ]. NB determines the class membership degree based on a given class designation [ 40 ]. It scans the data once, and thus, classification is easy [ 41 ]. Simply, the NB classifier assumes that there is no relation between the presence of a particular feature in a class and the presence of any other characteristic. It is mainly targeted at the text classification industry [ 42 ].

NB has great benefits such as ease of implementation, can provide a good result even using fewer training data, can manage both continuous and discrete data, and is ideal to solve the prediction of multi-class problems, and the irrelevant feature does not affect the prediction. NB, on the other hand, has the following drawbacks: It assumes that all features are independent which is not always viable in real-world problems, suffers from zero frequency problems, and the prediction of NB is not usually accurate. Implementation tools are WEKA, Python, RStudio, and Mahout [ 22 ].

To summarize the previously discussed models, Table 1 demonstrates the advantages and disadvantages of each model.

Unsupervised learning

Unlike supervised learning, there are no correct answers and no teachers in unsupervised learning [ 42 ]. It follows the concept that a machine can learn to understand complex processes and patterns on its own without external guidance. This approach is particularly useful in cases where experts have no knowledge of what to look for in the data and the data itself do not include the objectives. The machine predicts the outcome based on past experiences and learns to predict the real-valued outcome from the information previously provided, as shown in Fig.  9 .

Workflow of unsupervised learning [ 23 ]

Unsupervised learning is widely used in the processing of multimedia content, as clustering and partitioning of data in the lack of class labels is often a requirement [ 43 ]. Some of the most popular unsupervised learning-based approaches are k-means, principal component analysis (PCA), and apriori algorithm.

The k-means algorithm is the common portioning method [ 44 ] and one of the most popular unsupervised learning algorithms that deal with the well-known clustering problem. The procedure classifies a particular dataset by a certain number of preselected (assuming k -sets) clusters [ 45 ]. The pseudocode of the K-means algorithm is shown in Pseudocode 1.

K means has several benefits such as being more computationally efficient than hierarchical grouping in case of large variables. It provides more compact clusters than hierarchical ones when a small k is used. Also, the ease of implementation and comprehension of assembly results is another benefit. However, K -means have disadvantages such as the difficulty of predicting the value of K . Also, as different starting sections lead to various final combinations, the performance is affected. It is accurate for raw points and local optimization, and there is no single solution for a given K value—so the average of the K value must be run multiple times (20–100 times) and then pick the results with the minimum J [ 19 ].

B. Principal component analysis

In modern data analysis, principal component analysis (PCA) is an essential tool as it provides a guide for extracting the most important information from a dataset, compressing the data size by keeping only those important features without losing much information, and simplifying the description of a dataset [ 46 , 47 ].

PCA is frequently used to reduce data dimensions before applying classification models. Moreover, unsupervised methods, such as dimensionality reduction or clustering algorithms, are commonly used for data visualizations, detection of common trends or behaviors, and decreasing the data quantity to name a few only [ 48 ].

PCA converts the 2D data into 1D data. This is done by changing the set of variables into new variables known as principal components (PC) which are orthogonal [ 23 ]. In PCA, data dimensions are reduced to make calculations faster and easier. To illustrate how PCA works, let us consider an example of 2D data. When these data are plotted on a graph, it will take two axes. Applying PCA, the data turn into 1D. This process is illustrated in Fig.  10 [ 49 ].

Visualization of data before and after applying PCA [ 49 ]

Apriori algorithm is considered an important algorithm, which was first introduced by R. Agrawal and R. Srikant, and published in [ 50 , 51 ].

The principle of the apriori algorithm is to represent the filter generation strategy. It creates a filter element set ( k  + 1) based on the repeated k element groups. Apriori uses an iterative strategy called planar search, where k item sets are employed to explore ( k  + 1) item sets. First, the set of repeating 1 item is produced by scanning the dataset to collect the number for each item and then collecting items that meet the minimum support. The resulting group is called L1. Then L1 is used to find L2, the recursive set of two elements is used to find L3, and so on until no repeated k element groups are found. Finding every Lk needs a full dataset scan. To improve production efficiency at the level-wise of repeated element groups, a key property called the apriori property is used to reduce the search space. Apriori property states that all non-empty subsets of a recursive element group must be iterative. A two-step technique is used to identify groups of common elements: join and prune activities [ 52 ].

Although it is simple, the apriori algorithm suffers from several drawbacks. The main limitation is the costly wasted time to contain many candidates sets with a lot of redundant item sets. It also suffers from low minimum support or large item sets, and multiple rounds of data are needed for data mining which usually results in irrelevant items, in addition to difficulties in discovering individual elements of events [ 53 , 54 ].

To summarize the previously discussed models, Table 2 demonstrates the advantages and disadvantages of each model.

Reinforcement learning

Reinforcement learning (RL) is different from supervised learning and unsupervised learning. It is a goal-oriented learning approach. RL is closely related to an agent (controller) that takes responsibility for the learning process to achieve a goal. The agent chooses actions, and as a result, the environment changes its state and returns rewards. Positive or negative numerical values are used as rewards. An agent's goal is to maximize the rewards accumulated over time. A job is a complete environment specification that identifies how to generate rewards [ 55 ]. Some of the most popular reinforcement learning-based algorithms are the Q-learning algorithm and the Monte Carlo tree search (MCTS).

A. Q-learning

Q-learning is a type of model-free RL. It can be considered an asynchronous dynamic programming approach. It enables agents to learn how to operate optimally in Markovian domains by exploring the effects of actions, without the need to generate domain maps [ 56 ]. It represented an incremental method of dynamic programming that imposed low computing requirements. It works through the successive improvement of the assessment of individual activity quality in particular states [ 57 ].

In information theory, Q-learning is strongly employed, and other related investigations are underway. Recently, Q-learning combined with information theory has been employed in different disciplines such as natural language processing (NLP), pattern recognition, anomaly detection, and image classification [ 58 , 59 , 60 , 60 ]. Moreover, a framework has been created to provide a satisfying response based on the user’s utterance using RL in a voice interaction system [ 61 ]. Furthermore, a high-resolution deep learning-based prediction system for local rainfall has been constructed [ 62 ].

The advantage of developmental Q-learning is that it is possible to identify the reward value effectively on a given multi-agent environment method as agents in ant Q-learning are interacting with each other. The problem with Q-learning is that its output can be stuck in the local minimum as agents just take the shortest path [ 63 ].

B. Monte Carlo tree search

Monte Carlo tree search (MCTS) is an effective technique for solving sequential selection problems. Its strategy is based on a smart tree search that balances exploration and exploitation. MCTS presents random samples in the form of simulations and keeps activity statistics for better educated choices in each future iteration. MCTS is a decision-making algorithm that is employed in searching tree-like huge complex regions. In such trees, each node refers to a state, which is also referred to as problem configuration, while edges represent transitions from one state to another [ 64 ].

The MCTS is related directly to cases that can be represented by a Markov decision process (MDP), which is a type of discrete-time random control process. Some modifications of the MCTS make it possible to apply it to partially observable Markov decision processes (POMDP) [ 65 ]. Recently, MCTS coupled with deep RL became the base of AlphaGo developed by Google DeepMind and documented in [ 66 ]. The basic MCTS method is conceptually simple, as shown in Fig.  11 .

Basic MCTS process

Tree 1 is constructed progressively and unevenly. The tree policy is utilized to get the critical node of the current tree for each iteration of the method. The tree strategy seeks to strike a balance between exploration and exploitation concerns. Then, from the specified node, simulation 2 is run, and the search tree is then updated according to the obtained results. This comprises adding a child node that matches the specified node's activity and updating its ancestor's statistics. During this simulation, movements are performed based on some default policy, which in its simplest case is to make uniform random movements. The benefit of MCTS is that there is no need to evaluate the values of the intermediate state, which significantly minimizes the amount of required knowledge in the field [ 67 ].

To summarize the previously discussed models, Table 3 demonstrates the advantages and disadvantages of each model.

Deep learning

Over the past decades, ML has had a significant impact on our daily lives with examples including efficient computer vision, web search, and recognition of optical characters. In addition, by applying ML approaches, AI at the human level has also been improved [ 68 , 69 , 70 ]. However, when it comes to the mechanisms of human information processing (such as sound and vision), the performance of traditional ML algorithms is far from satisfactory. The idea of deep learning (DL) was formed in the late 20th inspired by the deep hierarchical structures of human voice recognition and production systems. DL breaks have been introduced in 2006 when Hinton built a deep-structured learning architecture called deep belief network (DBN) [ 71 ].

The performance of classifiers using DL has been extensively improved with the increased complexity of data compared to classical learning methods. Figure  12 shows the performance of classic ML algorithms and DL methods [ 72 ]. The performance of typical ML algorithms becomes stable when they reach the training data threshold, but DL improves its performance as the complexity of data increases [ 73 ].

Performance of deep learning concerning the complexity of data

DL (deep ML, or deep-structured learning) is a subset of ML that involves a collection of algorithms attempting to represent high-level abstractions for data through a model that has complicated structures or is otherwise, composed of numerous nonlinear transformations. The most important characteristic of DL is the depth of the network. Another essential aspect of DL is the ability to replace handcrafted features generated by efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction [ 74 ].

DL has significantly advanced the latest technologies in a variety of applications, including machine translation, speech, and visual object recognition, NLP, and text automation, using multilayer artificial neural networks (ANNs) [ 15 ].

Different DL designs in the past two decades give enormous potential for employment in various sectors such as automatic voice recognition, computer vision, NLP, and bioinformatics. This section discusses the most common architectures of DL such as convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent convolution neural networks (RCNNs) [ 75 ].

A. Convolutional neural network

CNNs are special types of neural networks inspired by the human visual cortex and used in computer vision. It is an automatic feed-forward neural network in which information transfers exclusively in the forward direction [ 76 ]. CNN is frequently applied in face recognition, human organ localization, text analysis, and biological image recognition [ 77 ].

Since CNN was first created in 1989, it has done well in disease diagnosis over the past three decades [ 78 ]. Figure  13 depicts the general architecture of a CNN composed of feature extractors and a classifier. Each layer of the network accepts the output of the previous layer as input and passes it on to the next layer in feature extraction layers. A typical CNN architecture consists of three types of layers: convolution, pooling, and classification. There are two types of layers at the network's low and middle levels: convolutional layers and pooling layers. Even-numbered layers are used for convolutions, while odd-numbered layers are used for pooling operations. The convolution and pooling layers' output nodes are categorized in a two-dimensional plane called feature mapping. Each layer level is typically generated by combining one or more previous layers [ 79 ].

Architecture of CNN [ 79 ]

CNN has a lot of benefits, including a human optical processing system, greatly improved 2D and 3D image processing structure, and is effective in learning and extracting abstract information from 2D information. The max-pooling layer in CNN is efficient in absorbing shape anisotropy. Furthermore, they are constructed from sparse connections with paired weights and contain far fewer parameters than a fully connected network of equal size. CNNs are trained using a gradient-based learning algorithm and are less susceptible to the diminishing gradient problem because the gradient-based approach trains the entire network to directly reduce the error criterion, allowing CNNs to provide highly optimized weights [ 79 ].

B. Long short-term memory

LSTM is a special type of recurrent neural network (RNN) with internal memory and multiplicative gates. Since the original LSTM introduction in 1997 by Sepp Hochrieiter and Jürgen Schmidhuber, a variety of LSTM cell configurations have been described [ 80 ].

LSTM has contributed to the development of well-known software such as Alexa, Siri, Cortana, Google Translate, and Google voice assistant [ 81 ]. LSTM is an implementation of RNN with a special connection between nodes. The special components within the LSTM unit include the input, output, and forget gates. Figure  14 depicts a single LSTM cell.

LSTM unit [ 82 ]

x t  = Input vector at the time t.

h t-1  = Previous hidden state.

c t-1  = Previous memory state.

h t  = Current hidden state.

c t  = Current memory state.

[ x ] = Multiplication operation.

[+] = Addition operation.

LSTM is an RNN module that handles gradient loss problems. In general, RNN uses LSTM to eliminate propagation errors. This allows the RNN to learn over multiple time steps. LSTM is characterized by cells that hold information outside the recurring network. This cell enables the RNN to learn over many time steps. The basic principle of LSTMs is the state of the cell, which contains information outside the recurrent network. A cell is like a memory in a computer, which decides when data should be stored, written, read, or erased via the LSTM gateway [ 82 ]. Many network architectures use LSTM such as bidirectional LSTM, hierarchical and attention-based LSTM, convolutional LSTM, autoencoder LSTM, grid LSTM, cross-modal, and associative LSTM [ 83 ].

Bidirectional LSTM networks move the state vector forward and backward in both directions. This implies that dependencies must be considered in both temporal directions. As a result of inverse state propagation, the expected future correlations can be included in the network's current output [ 84 ]. Bidirectional LSTM investigates and analyzes this because it encapsulates spatially and temporally scattered information and can tolerate incomplete inputs via a flexible cell state vector propagation communication mechanism. Based on the detected gaps in data, this filtering mechanism reidentifies the connections between cells for each data sequence. Figure  15 depicts the architecture. A bidirectional network is used in this study to process properties from multiple dimensions into a parallel and integrated architecture [ 83 ].

(left) Bidirectional LSTM and (right) filter mechanism for processing incomplete data [ 84 ]

Hierarchical LSTM networks solve multi-dimensional problems by breaking them down into subproblems and organizing them in a hierarchical structure. This has the advantage of focusing on a single or multiple subproblems. This is accomplished by adjusting the weights within the network to generate a certain level of interest [ 83 ]. A weighting-based attention mechanism that analyzes and filters input sequences is also used in hierarchical LSTM networks for long-term dependency prediction [ 85 ].

Convolutional LSTM reduces and filters input data collected over a longer period using convolutional operations applied in LSTM networks or the LSTM cell architecture directly. Furthermore, due to their distinct characteristics, convolutional LSTM networks are useful for modeling many quantities such as spatially and temporally distributed relationships. However, many quantities can be expected collectively in terms of reduced feature representation. Decoding or decoherence layers are required to predict different output quantities not as features but based on their parent units [ 83 ].

The LSTM autoencoder solves the problem of predicting high-dimensional parameters by shrinking and expanding the network [ 86 ]. The autoencoder architecture is separately trained with the aim of accurate reconstruction of the input data as reported in [ 87 ]. Only the encoder is used during testing and commissioning to extract the low-dimensional properties that are transmitted to the LSTM. The LSTM was extended to multimodal prediction using this strategy. To compress the input data and cell states, the encoder and decoder are directly integrated into the LSTM cell architecture. This combined reduction improves the flow of information in the cell and results in an improved cell state update mechanism for both short-term and long-term dependency [ 83 ].

Grid long short-term memory is a network of LSTM cells organized into a multi-dimensional grid that can be applied to sequences, vectors, or higher-dimensional data like images [ 88 ]. Grid LSTM has connections to the spatial or temporal dimensions of input sequences. Thus, connections of different dimensions within cells extend the normal flow of information. As a result, grid LSTM is appropriate for the parallel prediction of several output quantities that may be independent, linear, or nonlinear. The network's dimensions and structure are influenced by the nature of the input data and the goal of the prediction [ 89 ].

A novel method for the collaborative prediction of numerous quantities is the cross-modal and associative LSTM. It uses several standard LSTMs to separately model different quantities. To calculate the dependencies of the quantities, these LSTM streams communicate with one another via recursive connections. The chosen layers' outputs are added as new inputs to the layers before and after them in other streams. Consequently, a multimodal forecast can be made. The benefit of this approach is that the correlation vectors that are produced have the same dimensions as the input vectors. As a result, neither the parameter space nor the computation time increases [ 90 ].

C. Recurrent convolution neural network

CNN is a key method for handling various computer vision challenges. In recent years, a new generation of CNNs has been developed, the recurrent convolution neural network (RCNN), which is inspired by large-scale recurrent connections in the visual systems of animals. The recurrent convolutional layer (RCL) is the main feature of RCNN, which integrates repetitive connections among neurons in the normal convolutional layer. With the increase in the number of repetitive computations, the receptive domains (RFs) of neurons in the RCL expand infinitely, which is contrary to biological facts [ 91 ].

The RCNN prototype was proposed by Ming Liang and Xiaolin Hu [ 92 , 93 ], and the structure is illustrated in Fig.  16 , in which both forward and redundant connections have local connectivity and weights shared between distinct sites. This design is quite like the recurrent multilayer perceptron (RMLP) concept which is often used for dynamic control [ 94 , 95 ] (Fig.  17 , middle). Like the distinction between MLP and CNN, the primary distinction is that in RMLP, common local connections are used in place of full connections. For this reason, the proposed model is known as RCNN [ 96 ].

Illustration of the architectures of CNN, RMLP, and RCNN [ 85 ]

Illustration of the total number of reviewed papers

The main unit of RCNN is the RCL. RCLs develop through discrete time steps. RCNN offers three basic advantages. First, it allows each unit to accommodate background information in an arbitrarily wide area in the current layer. Second, recursive connections improve the depth of the network while keeping the number of mutable parameters constant through weight sharing. This is consistent with the trend of modern CNN architecture to grow deeper with a relatively limited number of parameters. The third aspect of RCNN is the time exposed in RCNN which is a CNN with many paths between the input layer and the output layer, which makes learning simple. On one hand, having longer paths makes it possible for the model to learn very complex features. On the other hand, having shorter paths may improve the inverse gradient during training [ 91 ].

To summarize the previously discussed models, Table 4 demonstrates the advantages and disadvantages of each model.

Disease prediction with analytics

The studies discussed in this paper have been presented and published in high-quality journals and international conferences published by IEEE, Springer, and Elsevier, and other major scientific publishers such as Hindawi, Frontiers, Taylor, and MDPI. The search engines used are Google Scholar, Scopus, and Science Direct. All papers selected covered the period from 2019 to 2022. Machine learning, deep learning, health care, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. For this survey, empirical and review articles on the topics were considered. This section discusses existing research efforts that healthcare prediction using various techniques in ML and DL. This survey gives a detailed discussion about the methods and algorithms which are used for predictions, performance metrics, and tools of their model.

ML-based healthcare prediction

To predict diabetes patients, the authors of [ 97 ] utilized a framework to develop and evaluate ML classification models like logistic regression, KNN, SVM, and RF. ML method was implemented on the Pima Indian Diabetes Database (PIDD) which has 768 rows and 9 columns. The forecast accuracy delivers 83%. Results of the implementation approach indicate how the logistic regression outperformed other algorithms of ML, in addition only a structured dataset was selected but unstructured data are not considered, also model should be implemented in other healthcare domains like heart disease, and COVID-19, finally other factors should be considered for diabetes prediction, like family history of diabetes, smoking habits, and physical inactivity.

The authors created a diagnosis system in [ 98 ] that uses two different datasets (Frankfurt Hospital in Germany and PIDD provided by the UCI ML repository) and four prediction models (RF, SVM, NB, and DT) to predict diabetes. the SVM algorithm performed with an accuracy of 83.1 percent. There are some aspects of this study that need to be improved; such as, using a DL approach to predict diabetes may lead to achieving better results; furthermore, the model should be tested in other healthcare domains such as heart disease and COVID-19 prediction datasets.

In [ 99 ], the authors proposed three ML methods (logistic regression, DT, and boosted RF) to assess COVID-19 using OpenData Resources from Mexico and Brazil. To predict rescue and death, the proposed model incorporates just the COVID-19 patient's geographical, social, and economic conditions, as well as clinical risk factors, medical reports, and demographic data. On the dataset utilized, the model for Mexico has a 93 percent accuracy, and an F1 score is 0.79. On the other hand, on the used dataset, the Brazil model has a 69 percent accuracy and an F1 score is 0.75. The three ML algorithms have been examined and the acquired results showed that logistic regression is the best way of processing data. The authors should be concerned about the usage of authentication and privacy management of the created data.

A new model for predicting type 2 diabetes using a network approach and ML techniques was presented by the authors in [ 100 ] (logistic regression, SVM, NB, KNN, decision tree, RF, XGBoost, and ANN). To predict the risk of type 2 diabetes, the healthcare data of 1,028 type 2 diabetes patients and 1,028 non-type 2 diabetes patients were extracted from de-identified data. The experimental findings reveal the models’ effectiveness with an area under curve (AUC) varied from 0.79 to 0.91. The RF model achieved higher accuracy than others. This study relies only on the dataset providing hospital admission and discharge summaries from one insurance company. External hospital visits and information from other insurance companies are missing for people with many insurance providers.

The authors of [ 101 ] proposed a healthcare management system that can be used by patients to schedule appointments with doctors and verify prescriptions. It gives support for ML to detect ailments and determine medicines. ML models including DT, RF, logistic regression, and NB classifiers are applied to the datasets of diabetes, heart disease, chronic kidney disease, and liver. The results showed that among all the other models, logistic regression had the highest accuracy of 98.5 percent in the heart dataset. while the least accuracy is of the DT classifier which came out to be 92 percent. In the liver dataset the logistic regression with maximum accuracy of 75.17% among all others. In the chronic renal disease dataset, the logistic regression, RF, and Gaussian NB, all performed well with an accuracy of 1, the accuracy of 100% should be verified by using k-fold cross-validation to test the reliability of the models. In the diabetes dataset random forest with maximum accuracy of 83.67 percent. The authors should include a hospital directory as then various hospitals and clinics can be accessed through a single portal. Additionally, image datasets could be included to allow image processing of reports and the deployment of DL to detect diseases.

In [ 102 ], the authors developed an ML model to predict the occurrence of Type 2 Diabetes in the following year (Y + 1) using factors in the present year (Y). Between 2013 and 2018, the dataset was obtained as an electronic health record from a private medical institute. The authors applied logistic regression, RF, SVM, XGBoost, and ensemble ML algorithms to predict the outcome of non-diabetic, prediabetes, and diabetes. Feature selection was applied to choose the three classes efficiently. FPG, HbA1c, triglycerides, BMI, gamma-GTP, gender, age, uric acid, smoking, drinking, physical activity, and family history were among the features selected. According to the experimental results, the maximum accuracy was 73% from RF, while the lowest was 71% from the logistic regression model. The authors presented a model that used only one dataset. As a result, additional data sources should be applied to verify the models developed in this study.

The authors of [ 103 ] classified the diabetes dataset using SVM and NB algorithms with feature selection to improve the model's accuracy. PIDD is taken from the UCI Repository for analysis. For training and testing purposes the authors employed the k-fold cross-validation model, the SVM classifier was performing better than the NB method it offers around 91% correct predictions; however, the authors acknowledge that they need to extend to the latest dataset that will contain additional attributes and rows.

K-means clustering is an unsupervised ML algorithm that was introduced by the authors of [ 104 ] for the purpose of detecting heart disease in its earliest stages using the UCI heart disease dataset. PCA is used for dimensionality reduction. The outcome of the method demonstrates early cardiac disease prediction with 94.06% accuracy. The authors should apply the proposed technique using more than one algorithm and use more than one dataset.

In [ 105 ], the authors constructed a predictive model for the classification of diabetes data using the logistic regression classification technique. The dataset includes 459 patients for training data and 128 cases for testing data. The prediction accuracy using logistic regression was obtained at 92%. The main limitation of this research is that the authors have not compared the model with other diabetes prediction algorithms, so it cannot be confirmed.

The authors of [ 106 ] developed a prediction model that analyzes the user's symptoms and predicts the disease using ML algorithms (DT classifier, RF classifier, and NB classifier). The purpose of this study was to solve health-related problems by allowing medical professionals to predict diseases at an early stage. The dataset is a sample of 4920 patient records with 41 illnesses diagnosed. A total of 41 disorders were included as a dependent variable. All algorithms achieved the same accuracy score of 95.12%. The authors noticed that overfitting occurred when all 132 symptoms from the original dataset were assessed instead of 95 symptoms. That is, the tree appears to remember the dataset provided and thus fails to classify new data. As a result, just 95 symptoms were assessed during the data-cleansing process, with the best ones being chosen.

In [ 107 ], the authors built a decision-making system that assists practitioners to anticipate cardiac problems in exact classification through a simpler method and will deliver automated predictions about the condition of the patient’s heart. implemented 4 algorithms (KNN, RF, DT, and NB), all these algorithms were used in the Cleveland Heart Disease dataset. The accuracy varies for different classification methods. The maximum accuracy is given when they utilized the KNN algorithm with the Correlation factor which is almost 94 percent. The authors should extend the presented technique to leverage more than one dataset and forecast different diseases.

The authors of [ 108 ] used the Cleveland dataset, which included 303 cases and 76 attributes, to test out three different classification strategies: NB, SVM, and DT in addition to KNN. Only 14 of these 76 characteristics are going to be put through the testing process. The authors performed data preprocessing to remove noisy data. The KNN obtained the greatest accuracy with 90.79 percent. The authors need to use more sophisticated models to improve the accuracy of early heart disease prediction.

The authors of [ 109 ] proposed a model to predict heart disease by making use of a cardiovascular dataset, which was then classified through the application of supervised machine learning algorithms (DT, NB, logistic regression, RF, SVM, and KNN). The results reveal that the DT classification model predicted cardiovascular disorders better than other algorithms with an accuracy of 73 percent. The authors highlighted that the ensemble ML techniques employing the CVD dataset can generate a better illness prediction model.

In [ 110 ], the authors attempted to increase the accuracy of heart disease prediction by applying a logistic regression using a healthcare dataset to determine whether patients have heart illness problems or not. The dataset was acquired from an ongoing cardiovascular study on people of the town of Framingham, Massachusetts. The model reached an accuracy prediction of 87 percent. The authors acknowledge the model could be improved with more data and the use of more ML models.

Because breast cancer affects one in every 28 women in India, the author of [ 111 ] presented an accurate classification technique to examine the breast cancer dataset containing 569 rows and 32 columns. Similarly employing a heart disease dataset and Lung cancer dataset, this research offered A novel way to function selection. This method of selection is based on genetic algorithms mixed with the SVM classification. The classifier results are Lung cancer 81.8182, Diabetes 78.9272. noticed that the size, kind, and source of data used are not indicated.

In [ 112 ], the authors predicted the risk factors that cause heart disease using the K-means clustering algorithm and analyzed with a visualization tool using a Cleveland heart disease dataset with 76 features of 303 patients, holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate as well as four types of chest pain. The authors forecast cardiac diseases by taking into consideration the primary characteristics of four types of chest discomfort solely and K-means clustering is a common unsupervised ML technique.

The aim of the article [ 113 ] was to report the advantages of using a variety of data mining (DM) methods and validated heart disease survival prediction models. From the observations, the authors proposed that logistic regression and NB achieved the highest accuracy when performed on a high-dimensional dataset on the Cleveland hospital dataset and DT and RF produce better results on low-dimensional datasets. RF delivers more accuracy than the DT classifier as the algorithm is an optimized learning algorithm. The author mentioned that this work can be extended to other ML algorithms, the model could be developed in a distributed environment such as Map–Reduce, Apache Mahout, and HBase.

In [ 114 ], the authors proposed a single algorithm named hybridization to predict heart disease that combines used techniques into one single algorithm. The presented method has three phases. Preprocessing phase, classification phase, and diagnosis phase. They employed the Cleveland database and algorithms NB, SVM, KNN, NN, J4.8, RF, and GA. NB and SVM always perform better than others, whereas others depend on the specified features. results attained an accuracy of 89.2 percent. The authors need to is the key goal. Notice that the dataset is little; hence, the system was not able to train adequately, so the accuracy of the method was bad.

Using six algorithms (logistic regression, KNN, DT, SVM, NB, and RF), the authors of [ 115 ] explored different data representations to better understand how to use clinical data for predicting liver disease. The original dataset was taken from the northeast of Andhra Pradesh, India. includes 583 liver patient data, whereas 75.64 percent are male, and 24.36 percent are female. The analysis result indicated that the logistic regression classifier delivers the most increased order exactness of 75 percent depending on the f1 measure to forecast the liver illness and NB gives the least precision of 53 percent. The authors merely studied a few prominent supervised ML algorithms; more algorithms can be picked to create an increasingly exact model of liver disease prediction and performance can be steadily improved.

In [ 116 ], the authors aimed to predict coronary heart disease (CHD) based on historical medical data using ML technology. The goal of this study is to use three supervised learning approaches, NB, SVM, and DT, to find correlations in CHD data that could aid improve prediction rates. The dataset contains a retrospective sample of males from KEEL, a high-risk heart disease location in the Western Cape of South Africa. The model utilized NB, SVM, and DT. NB achieved the most accurate among the three models. SVM and DT J48 outperformed NB with a specificity rate of 82 percent but showed an inadequate sensitivity rate of less than 50 percent.

With the help of DM and network analysis methods, the authors of [ 117 ] created a chronic disease risk prediction framework that was created and evaluated in the Australian healthcare system to predict type 2 diabetes risk. Using a private healthcare funds dataset from Australia that spans six years and three different predictive algorithms (regression, parameter optimization, and DT). The accuracy of the prediction ranges from 82 to 87 percent. The hospital admission and discharge summary are the dataset's source. As a result, it does not provide information about general physician visits or future diagnoses.

DL-based healthcare prediction

With the help of DL algorithms such as CNN for autofeature extraction and illness prediction, the authors of [ 118 ] proposed a system for predicting patients with the more common inveterate diseases, and they used KNN for distance calculation to locate the exact matching in the dataset and the outcome of the final sickness prediction. A combination of disease symptoms was made for the structure of the dataset, the living habits of a person, and the specific attaches to doctor consultations which are acceptable in this general disease prediction. In this study, the Indian chronic kidney disease dataset was utilized that comprises 400 occurrences, 24 characteristics, and 2 classes were restored from the UCI ML store. Finally, a comparative study of the proposed system with other algorithms such as NB, DT, and logistic regression has been demonstrated in this study. The findings showed that the proposed system gives an accuracy of 95% which is higher than the other two methods. So, the proposed technique should be applied using more than one dataset.

In [ 119 ], the authors developed a DL approach that uses chest radiography images to differentiate between patients with mild, pneumonia, and COVID-19 infections, providing a valid mechanism for COVID-19 diagnosis. To increase the intensity of the chest X-ray image and eliminate noise, image-enhancing techniques were used in the proposed system. Two distinct DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 identification utilizing chest X-ray (CXR) pictures are proposed in this work to minimize overfitting and increase the overall capabilities of the suggested DL systems. The authors emphasized that tests using a vast and hard dataset encompassing several COVID-19 cases are necessary to establish the efficacy of the suggested system.

Diabetes disease prediction was the topic of the article [ 120 ], in which the authors presented a cuckoo search-based deep LSTM classifier for prediction. The deep convLSTM classifier is used in cuckoo search optimization, which is a nature-inspired method for accurately predicting disease by transferring information and therefore reducing time consumption. The PIMA dataset is used to predict the onset of diabetes. The National Institute of Diabetes and Digestive and Kidney Diseases provided the data. The dataset is made up of independent variables including insulin level, age, and BMI index, as well as one dependent variable. The new technique was compared to traditional methods, and the results showed that the proposed method achieved 97.591 percent accuracy, 95.874 percent sensitivity, and 97.094 percent specificity, respectively. The authors noticed more datasets are needed, as well as new approaches to improve the classifier's effectiveness.

In [ 121 ], the authors presented a wavelet-based convolutional neural network to handle data limitations in this time of COVID-19 fast emergence. By investigating the influence of discrete wavelet transform decomposition up to 4 levels, the model demonstrated the capability of multi-resolution analysis for detecting COVID-19 chest X-rays. The wavelet sub-bands are CNN’s inputs at each decomposition level. COVID-19 chest X-ray-12 is a collection of 1,944 chest X-ray pictures divided into 12 groups that were compiled from two open-source datasets (National Institute Health containing several X-rays of pneumonia-related diseases, whereas the COVID-19 dataset is collected from Radiology Society North America). COVID-Neuro wavelet, a suggested model, was trained alongside other well-known ImageNet pre-trained models on COVID-CXR-12. The authors acknowledge they hope to investigate the effects of other wavelet functions besides the Haar wavelet.

A CNN framework for COVID-19 identification was suggested in [ 122 ] it made use of computed tomography images that was developed by the authors. The proposed framework employs a public CT dataset of 2482 CT images from patients of both classifications. the system attained an accuracy of 96.16 percent and recall of 95.41 percent after training using only 20 percent of the dataset. The authors stated that the use of the framework should be extended to multimodal medical pictures in the future.

Using an LSTM network enhanced by two processes to perform multi-label classification based on patients' clinical visit records, the authors of [ 123 ] performed multi-disease prediction for intelligent clinical decision support. A massive dataset of electronic health records was collected from a prominent hospital in southeast China. The suggested LSTM approach outperforms several standard and DL models in predicting future disease diagnoses, according to model evaluation results. The F1 score rises from 78.9 to 86.4 percent, respectively, with the state-of-the-art conventional and DL models, to 88.0 percent with the suggested technique. The authors stated that the model prediction performance may be enhanced further by including new input variables and that to reduce computational complexity, the method only uses one data source.

In [ 124 ], the authors introduced an approach to creating a supervised ANN structure based on the subnets (the group of neurons) instead of layers, in the cases of low datasets, this effectively predicted the disease. The model was evaluated using textual data and compared to multilayer perceptrons (MLPs) as well as LSTM recurrent neural network models using three small-scale publicly accessible benchmark datasets. On the Iris dataset, the experimental findings for classification reached 97% accuracy, compared to 92% for RNN (LSTM) with three layers, and the model had a lower error rate, 81, than RNN (LSTM) and MLP on the diabetic dataset, while RNN (LSTM) has a high error rate of 84. For larger datasets, however, this method is useless. This model is useless because it has not been implemented on large textual and image datasets.

The authors of [ 125 ] presented a novel AI and Internet of Things (IoT) convergence-based disease detection model for a smart healthcare system. Data collection, reprocessing, categorization, and parameter optimization are all stages of the proposed model. IoT devices, such as wearables and sensors, collect data, which AI algorithms then use to diagnose diseases. The forest technique is then used to remove any outliers found in the patient data. Healthcare data were used to assess the performance of the CSO-LSTM model. During the study, the CSO-LSTM model had a maximum accuracy of 96.16% on heart disease diagnoses and 97.26% on diabetes diagnoses. This method offered a greater prediction accuracy for heart disease and diabetes diagnosis, but there was no feature selection mechanism; hence, it requires extensive computations.

The global health crisis posed by coronaviruses was a subject of [ 126 ]. The authors aimed at detecting disease in people whose X-ray had been selected as potential COVID-19 candidates. Chest X-rays of people with COVID-19, viral pneumonia, and healthy people are included in the dataset. The study compared the performance of two DL algorithms, namely CNN and RNN. DL techniques were used to evaluate a total of 657 chest X-ray images for the diagnosis of COVID-19. VGG19 is the most successful model, with a 95% accuracy rate. The VGG19 model successfully categorizes COVID-19 patients, healthy individuals, and viral pneumonia cases. The dataset's most failing approach is InceptionV3. The success percentage can be improved, according to the authors, by improving data collection. In addition to chest radiography, lung tomography can be used. The success ratio and performance can be enhanced by creating numerous DL models.

In [ 127 ], the authors developed a method based on the RNN algorithm for predicting blood glucose levels for diabetics a maximum of one hour in the future, which required the patient's glucose level history. The Ohio T1DM dataset for blood glucose level prediction, which included blood glucose level values for six people with type 1 diabetes, was used to train and assess the approach. The distribution features were further honed with the use of studies that revealed the procedure's certainty estimate nature. The authors point out that they can only evaluate prediction goals with enough glucose level history; thus, they cannot anticipate the beginning levels after a gap, which does not improve the prediction's quality.

To build a new deep anomaly detection model for fast, reliable screening, the authors of [ 128 ] used an 18-layer residual CNN pre-trained on ImageNet with a different anomaly detection mechanism for the classification of COVID-19. On the X-ray dataset, which contains 100 images from 70 COVID-19 persons and 1431 images from 1008 non-COVID-19 pneumonia subjects, the model obtains a sensitivity of 90.00 percent specificity of 87.84 percent or sensitivity of 96.00 percent specificity of 70.65 percent. The authors noted that the model still has certain flaws, such as missing 4% of COVID-19 cases and having a 30% false positive rate. In addition, more clinical data are required to confirm and improve the model's usefulness.

In [ 129 ], the authors developed COVIDX-Net, a novel DL framework that allows radiologists to diagnose COVID-19 in X-ray images automatically. Seven algorithms (MobileNetV2, ResNetV2, VGG19, DenseNet201, InceptionV3, Inception, and Xception) were evaluated using a small dataset of 50 photographs (MobileNetV2, ResNetV2, VGG19, DenseNet201, InceptionV3, Inception, and Xception). Each deep neural network model can classify the patient's status as a negative or positive COVID-19 case based on the normalized intensities of the X-ray image. The f1-scores for the VGG19 and dense convolutional network (DenseNet) models were 0.89 and 0.91, respectively. With f1-scores of 0.67, the InceptionV3 model has the weakest classification performance.

The authors of [ 130 ] designed a DL approach for delivering 30-min predictions about future glucose levels based on a Dilated RNN (DRNN). The performance of the DRNN models was evaluated using data from two electronic health records datasets: OhioT1DM from clinical trials and the in silicon dataset from the UVA-Padova simulator. It outperformed established glucose prediction approaches such as neural networks (NNs), support vector regression (SVR), and autoregressive models (ARX). The results demonstrated that it significantly improved glucose prediction performance, although there are still some limits, such as the authors' creation of a data-driven model that heavily relies on past EHR. The quality of the data has a significant impact on the accuracy of the prediction. The number of clinical datasets is limited and , however, often restricted. Because certain data fields are manually entered, they are occasionally incorrect.

In [ 131 ], the authors utilized a deep neural network (DNN) to discover 15,099 stroke patients, researchers were able to predict stroke death based on medical history and human behaviors utilizing large-scale electronic health information. The Korea Centers for Disease Control and Prevention collected data from 2013 to 2016 and found that there are around 150 hospitals in the country, all having more than 100 beds. Gender, age, type of insurance, mode of admission, necessary brain surgery, area, length of hospital stays, hospital location, number of hospital beds, stroke kind, and CCI were among the 11 variables in the DL model. To automatically create features from the data and identify risk factors for stroke, researchers used a DNN/scaled principal component analysis (PCA). 15,099 people with a history of stroke were enrolled in the study. The data were divided into a training set (66%) and a testing set (34%), with 30 percent of the samples used for validation in the training set. DNN is used to examine the variables of interest, while scaled PCA is utilized to improve the DNN's continuous inputs. The sensitivity, specificity, and AUC values were 64.32%, 85.56%, and 83.48%, respectively.

The authors of [ 132 ] proposed (GluNet), an approach to glucose forecasting. This method made use of a personalized DNN to forecast the probabilistic distribution of short-term measurements for people with Type 1 diabetes based on their historical data. These data included insulin doses, meal information, glucose measurements, and a variety of other factors. It utilized the newest DL techniques consisting of four components: post-processing, dilated CNN, label recovery/ transform, and data preprocessing. The authors run the models on the subjects from the OhioT1DM datasets. The outcomes revealed significant enhancements over the previous procedures via a comprehensive comparison concerning the and root mean square error (RMSE) having a time lag of 60 min prediction horizons (PH) and RMSE having a small-time lag for the case of prediction horizons in the virtual adult participants. If the PH is properly matched to the lag between input and output, the user may learn the control of the system more frequently and it achieves good performance. Additionally, GluNet was validated on two clinical datasets. It attained an RMSE with a time lag of 60 min PH and RMSE with a time lag of 30-min PH. The authors point out that the model does not consider physiological knowledge, and that they need to test GluNet with larger prediction horizons and use it to predict overnight hypoglycemia.

The authors of [ 133 ] proposed the short-term blood glucose prediction model (VMD-IPSO-LSTM), which is a short-term strategy for predicting blood glucose (VMD-IPSO-LSTM). Initially, the intrinsic modal functions (IMF) in various frequency bands were obtained using the variational modal decomposition (VMD) technique, which deconstructed the blood glucose content. The short- and long-term memory networks then constructed a prediction mechanism for each blood glucose component’s intrinsic modal functions (IMF). Because the time window length, learning rate, and neuron count are difficult to set, the upgraded PSO approach optimized these parameters. The improved LSTM network anticipated each IMF, and the projected subsequence was superimposed in the final step to arrive at the ultimate prediction result. The data of 56 participants were chosen as experimental data among 451 diabetic Mellitus patients. The experiments revealed that it improved prediction accuracy at "30 min, 45 min, and 60 min." The RMSE and MAPE were lower than the "VMD-PSO-LSTM, VMD-LSTM, and LSTM," indicating that the suggested model is effective. The longer time it took to anticipate blood glucose levels and the higher accuracy of the predictions gave patients and doctors more time to improve the effectiveness of diabetes therapy and manage blood glucose levels. The authors noted that they still faced challenges, such as an increase in calculation volume and operation time. The time it takes to estimate glucose levels in the short term will be reduced.

To speed up diagnosis and cut down on mistakes, the authors of [ 134 ] proposed a new paradigm for primary COVID-19 detection based on a radiology review of chest radiography or chest X-ray. The authors used a dataset of chest X-rays from verified COVID-19 patients (408 photographs), confirmed pneumonia patients (4273 images), and healthy people (1590 images) to perform a three-class image classification (1590 images). There are 6271 people in total in the dataset. To fulfill this image categorization problem, the authors plan to use CNN and transfer learning. For all the folds of data, the model's accuracy ranged from 93.90 percent to 98.37 percent. Even the lowest level of accuracy, 93.90 percent, is still quite good. The authors will face a restriction, particularly when it comes to adopting such a model on a large scale for practical usage.

In [ 135 ], the authors proposed DL models for predicting the number of COVID-19-positive cases in Indian states. The Ministry of Health and Family Welfare dataset contains time series data for 32 individual confirmed COVID-19 cases in each of the states (28) and union territories (4) since March 14, 2020. This dataset was used to conduct an exploratory analysis of the increase in the number of positive cases in India. As prediction models, RNN-based LSTMs are used. Deep LSTM, convolutional LSTM, and bidirectional LSTM models were tested on 32 states/union territories, and the model with the best accuracy was chosen based on absolute error. Bidirectional LSTM produced the best performance in terms of prediction errors, while convolutional LSTM produced the worst performance. For all states, daily and weekly forecasts were calculated, and bi-LSTM produced accurate results (error less than 3%) for short-term prediction (1–3 days).

With the goal of increasing the reliability and precision of type 1 diabetes predictions, the authors of [ 136 ] proposed a new method based on CNNs and DL. It was about figuring out how to extract the behavioral pattern. Numerous observations of identical behaviors were used to fill in the gaps in the data. The suggested model was trained and verified using data from 759 people with type 1 diabetes who visited Sheffield Teaching Hospitals between 2013 and 2015. A subject's type 1 diabetes test, demographic data (age, gender, years with diabetes), and the final 84 days (12 weeks) of self-monitored blood glucose (SMBG) measurements preceding the test formed each item in the training set. In the presence of insufficient data and certain physiological specificities, prediction accuracy deteriorates, according to the authors.

The authors of [ 137 ] constructed a framework using the PIDD. PID's participants are all female and at least 21 years old. PID comprises 768 incidences, with 268 samples diagnosed as diabetic and 500 samples not diagnosed as diabetic. The eight most important characteristics that led to diabetes prediction. The accuracy of functional classifiers such as ANN, NB, DT, and DL is between 90 and 98 percent. On the PIMA dataset, DL had the best results for diabetes onset among the four, with an accuracy rate of 98.07 percent. The technique uses a variety of classifiers to accurately predict the disease, but it failed to diagnose it at an early stage.

To summarize all previous works discussed in this section, we will categorize them according to the diseases along with the techniques used to predict each disease, the datasets used, and the main findings, as shown in Table 5 .

Results and discussion

This study conducted a systematic review to examine the latest developments in ML and DL for healthcare prediction. It focused on healthcare forecasting and how the use of ML and DL can be relevant and robust. A total of 41 papers were reviewed, 21 in ML and 20 in DL as depicted in Fig.  17 .

In this study, the reviewed paper were classified by diseases predicted; as a result, 5 diseases were discussed including diabetes, COVID-19, heart, liver, and chronic kidney). Table 6 illustrates the number of reviewed papers for each disease in addition to the adopted prediction techniques in each disease.

Table 6 provides a comprehensive summary of the various ML and DL models used for disease prediction. It indicates the number of studies conducted on each disease, the techniques employed, and the highest level of accuracy attained. As shown in Table 6 , the optimal diagnostic accuracy for each disease varies. For diabetes, the DL model achieved a 98.07% accuracy rate. For COVID-19, the accuracy of the logistic regression model was 98.5%. The CSO-LSTM model achieved an accuracy of 96.16 percent for heart disease. For liver disease, the accuracy of the logistic regression model was 75%. The accuracy of the logistic regression model for predicting multiple diseases was 98.5%. It is essential to note that these are merely the best accuracy included in this survey. In addition, it is essential to consider the size and quality of the datasets used to train and validate the models. It is more likely that models trained on larger and more diverse datasets will generalize well to new data. Overall, the results presented in Table 6 indicate that ML and DL models can be used to accurately predict disease. When selecting a model for a specific disease, it is essential to carefully consider the various models and techniques.

Although ML and DL have made incredible strides in recent years, they still have a long way to go before they can effectively be used to solve the fundamental problems plaguing the healthcare systems. Some of the challenges associated with implementing ML and DL approaches in healthcare prediction are discussed here.

The Biomedical Data Stream is the primary challenge that needs to be handled. Significant amounts of new medical data are being generated rapidly, and the healthcare industry as a whole is evolving rapidly. Some examples of such real-time biological signals include measurements of blood pressure, oxygen saturation, and glucose levels. While some variants of DL architecture have attempted to address this problem, many challenges remain before effective analyses of rapidly evolving, massive amounts of streaming data can be conducted. These include problems with memory consumption, feature selection, missing data, and computational complexity. Another challenge for ML and DL is tackling the complexity of the healthcare domain.

Healthcare and biomedical research present more intricate challenges than other fields. There is still a lot we do not know about the origins, transmission, and cures for many of these incredibly diverse diseases. It is hard to collect sufficient data because there are not always enough patients. A solution to this issue may be found, however. The small number of patients necessitates exhaustive patient profiling, innovative data processing, and the incorporation of additional datasets. Researchers can process each dataset independently using the appropriate DL technique and then represent the results in a unified model to extract patient data.

The use of ML and DL techniques for healthcare prediction has the potential to change the way traditional healthcare services are delivered. In the case of ML and DL applications, healthcare data is deemed the most significant component that contributes to medical care systems. This paper aims to present a comprehensive review of the most significant ML and DL techniques employed in healthcare predictive analytics. In addition, it discussed the obstacles and challenges of applying ML and DL Techniques in the healthcare domain. As a result of this survey, a total of 41 papers covering the period from 2019 to 2022 were selected and thoroughly reviewed. In addition, the methodology for each paper was discussed in detail. The reviewed studies have shown that AI techniques (ML and DL) play a significant role in accurately diagnosing diseases and helping to anticipate and analyze healthcare data by linking hundreds of clinical records and rebuilding a patient's history using these data. This work advances research in the field of healthcare predictive analytics using ML and DL approaches and contributes to the literature and future studies by serving as a resource for other academics and researchers.

Availability of data and materials

Not applicable.

Abbreviations

Artificial Intelligence

Machine Learning

Decision Tree

Electronic Health Records

Random Forest

Support Vector Machine

K-Nearest Neighbor

Naive Bayes

Reinforcement Learning

Natural Language Processing

Monte Carlo Tree Search

Partially Observable Markov Decision Processes

Deep Learning

Deep Belief Network

Artificial Neural Networks

Convolutional Neural Networks

Long Short-Term Memory

Recurrent Convolution Neural Networks

Recurrent Neural Networks

Recurrent Convolutional Layer

Receptive Domains

Recurrent Multilayer Perceptron

Pima Indian Diabetes Database

Coronary Heart Disease

Chest X-Ray

Multilayer Perceptrons

Internet of Things

Dilated RNN

Neural Networks

Support Vector Regression

Principal Component Analysis

Deep Neural Network

Prediction Horizons

Root Mean Square Error

Intrinsic Modal Functions

Variational Modal Decomposition

Self-Monitored Blood Glucose

Latha MH, Ramakrishna A, Reddy BSC, Venkateswarlu C, Saraswathi SY (2022) Disease prediction by stacking algorithms over big data from healthcare communities. Intell Manuf Energy Sustain: Proc ICIMES 2021(265):355

Google Scholar  

Van Calster B, Wynants L, Timmerman D, Steyerberg EW, Collins GS (2019) Predictive analytics in health care: how can we know it works? J Am Med Inform Assoc 26(12):1651–1654

Sahoo PK, Mohapatra SK, Wu SL (2018) SLA based healthcare big data analysis and computing in cloud network. J Parallel Distrib Comput 119:121–135

Thanigaivasan V, Narayanan SJ, Iyengar SN, Ch N (2018) Analysis of parallel SVM based classification technique on healthcare using big data management in cloud storage. Recent Patents Comput Sci 11(3):169–178

Elmahdy HN (2014) Medical diagnosis enhancements through artificial intelligence

Xiong X, Cao X, Luo L (2021) The ecology of medical care in Shanghai. BMC Health Serv Res 21:1–9

Donev D, Kovacic L, Laaser U (2013) The role and organization of health care systems. Health: systems, lifestyles, policies, 2nd edn. Jacobs Verlag, Lage, pp 3–144

Murphy G F, Hanken M A, & Waters K A (1999) Electronic health records: changing the vision

Qayyum A, Qadir J, Bilal M, Al-Fuqaha A (2020) Secure and robust machine learning for healthcare: a survey. IEEE Rev Biomed Eng 14:156–180

El Seddawy AB, Moawad R, Hana MA (2018) Applying data mining techniques in CRM

Wang Y, Kung L, Wang WYC, Cegielski CG (2018) An integrated big data analytics-enabled transformation model: application to health care. Inform Manag 55(1):64–79

Mirbabaie M, Stieglitz S, Frick NR (2021) Artificial intelligence in disease diagnostics: a critical review and classification on the current state of research guiding future direction. Heal Technol 11(4):693–731

Tang R, De Donato L, Besinović N, Flammini F, Goverde RM, Lin Z, Wang Z (2022) A literature review of artificial intelligence applications in railway systems. Transp Res Part C: Emerg Technol 140:103679

Singh G, Al’Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK, Dwivedi A, Maliakal G, Pandey M, Wang J, Do V (2018) Machine learning in cardiac CT: basic concepts and contemporary data. J Cardiovasc Comput Tomograph 12(3):192–201

Kim KJ, Tagkopoulos I (2019) Application of machine learning in rheumatic disease research. Korean J Intern Med 34(4):708

Liu B (2011) Web data mining: exploring hyperlinks, contents, and usage data. Spriger, Berlin

MATH   Google Scholar  

Haykin S, Lippmann R (1994) Neural networks, a comprehensive foundation. Int J Neural Syst 5(4):363–364

Gupta M, Pandya SD (2022) A comparative study on supervised machine learning algorithm. Int J Res Appl Sci Eng Technol (IJRASET) 10(1):1023–1028

Ray S (2019) A quick review of machine learning algorithms. In: 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp 35–39). IEEE

Srivastava A, Saini S, & Gupta D (2019) Comparison of various machine learning techniques and its uses in different fields. In: 2019 3rd international conference on electronics, communication and aerospace technology (ICECA) (pp 81–86). IEEE

Park HA (2013) An introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. J Korean Acad Nurs 43(2):154–164

Obulesu O, Mahendra M, & Thrilok Reddy M (2018) Machine learning techniques and tools: a survey. In: 2018 international conference on inventive research in computing applications (ICIRCA) (pp 605–611). IEEE

Dhall D, Kaur R, & Juneja M (2020) Machine learning: a review of the algorithms and its applications. Proceedings of ICRIC 2019: recent innovations in computing 47–63

Yang F J (2019) An extended idea about Decision Trees. In: 2019 international conference on computational science and computational intelligence (CSCI) (pp 349–354). IEEE

Eesa AS, Orman Z, Brifcani AMA (2015) A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst Appl 42(5):2670–2679

Shamim A, Hussain H, & Shaikh M U (2010) A framework for generation of rules from Decision Tree and decision table. In: 2010 international conference on information and emerging technologies (pp 1–6). IEEE

Eesa AS, Abdulazeez AM, Orman Z (2017) A dids based on the combination of cuttlefish algorithm and Decision Tree. Sci J Univ Zakho 5(4):313–318

Bakyarani ES, Srimathi H, Bagavandas M (2019) A survey of machine learning algorithms in health care. Int J Sci Technol Res 8(11):223

Resende PAA, Drummond AC (2018) A survey of random forest based methods for intrusion detection systems. ACM Comput Surv (CSUR) 51(3):1–36

Breiman L (2001) Random forests. Mach learn 45:5–32

Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

Hofmann M, & Klinkenberg R (2016) RapidMiner: data mining use cases and business analytics applications. CRC Press

Chow CKCN, Liu C (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans Inf Theory 14(3):462–467

Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

Han J, Pei J, Kamber M (1999) Data mining: concepts and techniques. 2011

Cortes C, Vapnik V (1995) Support-vector networks. Mach learn 20:273–297

Aldahiri A, Alrashed B, Hussain W (2021) Trends in using IoT with machine learning in health prediction system. Forecasting 3(1):181–206

Sarker IH (2021) Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci 2(3):160

Ting K M, & Zheng Z (1999) Improving the performance of boosting for naive Bayesian classification. In: Methodologies for knowledge discovery and data mining: third Pacific-Asia conference, PAKDD-99 Beijing, China, Apr 26–28, 1999 proceedings 3 (pp 296–305). Springer Berlin Heidelberg

Oladipo ID, AbdulRaheem M, Awotunde JB, Bhoi AK, Adeniyi EA, Abiodun MK (2022) Machine learning and deep learning algorithms for smart cities: a start-of-the-art review. In: IoT and IoE driven smart cities, pp 143–162

Shailaja K, Seetharamulu B, & Jabbar M A Machine learning in healthcare: a review. In: 2018 second international conference on electronics, communication and aerospace technology (ICECA) 2018 Mar 29 (pp 910–914)

Mahesh B (2020) Machine learning algorithms-a review. Int J Sci Res (IJSR) 9:381–386

Greene D, Cunningham P, & Mayer R (2008) Unsupervised learning and clustering. Mach learn Techn Multimed: Case Stud Organ Retriev 51–90

Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc, USA

Kodinariya TM, Makwana PR (2013) Review on determining number of cluster in K-means clustering. Int J 1(6):90–95

Smith LI (2002) A tutorial on principal components analysis

Mishra SP, Sarkar U, Taraphder S, Datta S, Swain D, Saikhom R, Laishram M (2017) Multivariate statistical data analysis-principal component analysis (PCA). Int J Livestock Res 7(5):60–78

Kamani M, Farzin Haddadpour M, Forsati R, and Mahdavi M (2019) "Efficient Fair Principal Component Analysis." arXiv e-prints: arXiv-1911.

Dey A (2016) Machine learning algorithms: a review. Int J Comput Sci Inf Technol 7(3):1174–1179

Agrawal R, Imieliński T, & Swami A (1993) Mining association rules between sets of items in large databases. In: proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp 207–216)

Agrawal R, & Srikant R (1994) Fast algorithms for mining association rules. In: Proceeding of 20th international conference very large data bases, VLDB (Vol 1215, pp 487-499)

Singh J, Ram H, Sodhi DJ (2013) Improving efficiency of apriori algorithm using transaction reduction. Int J Sci Res Publ 3(1):1–4

Al-Maolegi M, & Arkok B (2014) An improved Apriori algorithm for association rules. arXiv preprint arXiv:1403.3948

Abaya SA (2012) Association rule mining based on Apriori algorithm in minimizing candidate generation. Int J Sci Eng Res 3(7):1–4

Coronato A, Naeem M, De Pietro G, Paragliola G (2020) Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med 109:101964

Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8:279–292

Jang B, Kim M, Harerimana G, Kim JW (2019) Q-learning algorithms: a comprehensive classification and applications. IEEE access 7:133653–133667

Achille A, Soatto S (2018) Information dropout: Learning optimal representations through noisy computation. IEEE Trans Pattern Anal Mach Intell 40(12):2897–2905

Williams G, Wagener N, Goldfain B, Drews P, Rehg J M, Boots B, & Theodorou E A (2017) Information theoretic MPC for model-based reinforcement learning. In: 2017 IEEE international conference on robotics and automation (ICRA) (pp 1714–1721). IEEE

Wilkes JT, Gallistel CR (2017) Information theory, memory, prediction, and timing in associative learning. Comput Models Brain Behav 29:481–492

Ning Y, Jia J, Wu Z, Li R, An Y, Wang Y, Meng H (2017) Multi-task deep learning for user intention understanding in speech interaction systems. In: Proceedings of the AAAI conference on artificial intelligence (Vol 31, No. 1)

Shi X, Gao Z, Lausen L, Wang H, Yeung DY, Wong WK, Woo WC (2017) Deep learning for precipitation nowcasting: a benchmark and a new model. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (Eds) Advances in neural information processing systems, vol 30. Curran Associates, Inc.,. https://proceedings.neurips.cc/paper_files/paper/2017/file/a6db4ed04f1621a119799fd3d7545d3d-Paper.pdf

Juang CF, Lu CM (2009) Ant colony optimization incorporated with fuzzy Q-learning for reinforcement fuzzy control. IEEE Trans Syst, Man, Cybernet-Part A: Syst Humans 39(3):597–608

Świechowski M, Godlewski K, Sawicki B, Mańdziuk J (2022) Monte Carlo tree search: a review of recent modifications and applications. Artif Intell Rev 56:1–66

Lizotte DJ, Laber EB (2016) Multi-objective Markov decision processes for data-driven decision support. J Mach Learn Res 17(1):7378–7405

MathSciNet   MATH   Google Scholar  

Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489

Browne CB, Powley E, Whitehouse D, Lucas SM, Cowling PI, Rohlfshagen P, Colton S (2012) A survey of monte carlo tree search methods. IEEE Trans Comput Intell AI Games 4(1):1–43

Ling ZH, Kang SY, Zen H, Senior A, Schuster M, Qian XJ, Deng L (2015) Deep learning for acoustic modeling in parametric speech generation: a systematic review of existing techniques and future trends. IEEE Signal Process Magaz 32(3):35–52

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

Yu D, Deng L (2010) Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process Mag 28(1):145–154

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

Goyal P, Pandey S, Jain K, Goyal P, Pandey S, Jain K (2018) Introduction to natural language processing and deep learning. Deep Learn Nat Language Process: Creat Neural Netw Python 1–74. https://doi.org/10.1007/978-1-4842-3685-7

Mathew A, Amudha P, Sivakumari S (2021) Deep learning techniques: an overview. Adv Mach Learn Technol Appl: Proc AMLTA 2020:599–608

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, USA

Gomes L (2014) Machine-learning maestro Michael Jordan on the delusions of big data and other huge engineering efforts. IEEE Spectrum 20. https://spectrum.ieee.org/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts

Huang G, Liu Z, Van Der Maaten L, & Weinberger K Q (2017) Densely connected convolutional networks. In: proceedings of the IEEE conference on computer vision and pattern recognition (pp 4700–4708)

Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Marti R (2017) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218–1226

Hayashi Y (2019) The right direction needed to develop white-box deep learning in radiology, pathology, and ophthalmology: a short review. Front Robot AI 6:24

Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292

Schmidhuber J, Hochreiter S (1997) Long short-term memory. Neural Comput 9(8):1735–1780

Smagulova K, James AP (2019) A survey on LSTM memristive neural network architectures and applications. Eur Phys J Spec Topics 228(10):2313–2324

Setyanto A, Laksito A, Alarfaj F, Alreshoodi M, Oyong I, Hayaty M, Kurniasari L (2022) Arabic language opinion mining based on long short-term memory (LSTM). Appl Sci 12(9):4140

Lindemann B, Müller T, Vietz H, Jazdi N, Weyrich M (2021) A survey on long short-term memory networks for time series prediction. Procedia CIRP 99:650–655

Cui Z, Ke R, Pu Z, & Wang Y (2018) Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143

Villegas R, Yang J, Zou Y, Sohn S, Lin X, & Lee H (2017) Learning to generate long-term future via hierarchical prediction. In: international conference on machine learning (pp 3560–3569). PMLR

Gensler A, Henze J, Sick B, & Raabe N (2016) Deep learning for solar power forecasting—an approach using autoencoder and LSTM neural networks. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC) (pp 002858–002865). IEEE

Lindemann B, Fesenmayr F, Jazdi N, Weyrich M (2019) Anomaly detection in discrete manufacturing using self-learning approaches. Procedia CIRP 79:313–318

Kalchbrenner N, Danihelka I, & Graves A (2015) Grid long short-term memory. arXiv preprint arXiv:1507.01526

Cheng B, Xu X, Zeng Y, Ren J, Jung S (2018) Pedestrian trajectory prediction via the social-grid LSTM model. J Eng 2018(16):1468–1474

Veličković P, Karazija L, Lane N D, Bhattacharya S, Liberis E, Liò P & Vegreville M (2018) Cross-modal recurrent models for weight objective prediction from multimodal time-series data. In: proceedings of the 12th EAI international conference on pervasive computing technologies for healthcare (pp 178–186)

Wang J, Hu X (2021) Convolutional neural networks with gated recurrent connections. IEEE Trans Pattern Anal Mach Intell 44(7):3421–3435

Liang M, & Hu X (2015) Recurrent convolutional neural network for object recognition. In: proceedings of the IEEE conference on computer vision and pattern recognition (pp 3367–3375)

Liang M, Hu X, Zhang B (2015) Convolutional neural networks with intra-layer recurrent connections for scene labeling. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (Eds) Advances in Neural Information Processing Systems, vol 28. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2015/file/9cf81d8026a9018052c429cc4e56739b-Paper.pdf

Fernandez B, Parlos A G, & Tsai W K (1990) Nonlinear dynamic system identification using artificial neural networks (ANNs). In: 1990 IJCNN international joint conference on neural networks (pp 133–141). IEEE

Puskorius GV, Feldkamp LA (1994) Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Trans Neural Netw 5(2):279–297

Rumelhart DE (1986) Learning representations by error propagation. In: DE Rumelhart and JL McClelland & PDP Research Group, eds, Parallel distributed processing: explorations in the microstructure of cognition. Bradford Books MITPress, Cambridge, Mass

Krishnamoorthi R, Joshi S, Almarzouki H Z, Shukla P K, Rizwan A, Kalpana C, & Tiwari B (2022) A novel diabetes healthcare disease prediction framework using machine learning techniques. J Healthcare Eng. https://doi.org/10.1155/2022/1684017

Edeh MO, Khalaf OI, Tavera CA, Tayeb S, Ghouali S, Abdulsahib GM, Louni A (2022) A classification algorithm-based hybrid diabetes prediction model. Front Publ Health 10:829510

Iwendi C, Huescas C G Y, Chakraborty C, & Mohan S (2022) COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients. J Experiment Theor Artif Intell 1–21. https://doi.org/10.1080/0952813X.2022.2058097

Lu H, Uddin S, Hajati F, Moni MA, Khushi M (2022) A patient network-based machine learning model for disease prediction: the case of type 2 diabetes mellitus. Appl Intell 52(3):2411–2422

Chugh M, Johari R, & Goel A (2022) MATHS: machine learning techniques in healthcare system. In: international conference on innovative computing and communications: proceedings of ICICC 2021, Volume 3 (pp 693–702). Springer Singapore

Deberneh HM, Kim I (2021) Prediction of type 2 diabetes based on machine learning algorithm. Int J Environ Res Public Health 18(6):3317

Gupta S, Verma H K, & Bhardwaj D (2021) Classification of diabetes using Naive Bayes and support vector machine as a technique. In: operations management and systems engineering: select proceedings of CPIE 2019 (pp 365–376). Springer Singapore

Islam M T, Rafa S R, & Kibria M G (2020) Early prediction of heart disease using PCA and hybrid genetic algorithm with k-means. In: 2020 23rd international conference on computer and information technology (ICCIT) (pp 1–6). IEEE

Qawqzeh Y K, Bajahzar A S, Jemmali M, Otoom M M, Thaljaoui A (2020) Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Res Int. https://doi.org/10.1155/2020/3764653

Grampurohit S, Sagarnal C (2020) Disease prediction using machine learning algorithms. In: 2020 international conference for emerging technology (INCET) (pp 1–7). IEEE

Moturi S, Srikanth Vemuru DS (2020) Classification model for prediction of heart disease using correlation coefficient technique. Int J 9(2). https://doi.org/10.30534/ijatcse/2020/185922020

Barik S, Mohanty S, Rout D, Mohanty S, Patra A K, & Mishra A K (2020) Heart disease prediction using machine learning techniques. In: advances in electrical control and signal systems: select proceedings of AECSS 2019 (pp 879–888). Springer, Singapore

Princy R J P, Parthasarathy S, Jose P S H, Lakshminarayanan A R, & Jeganathan S (2020) Prediction of cardiac disease using supervised machine learning algorithms. In: 2020 4th international conference on intelligent computing and control systems (ICICCS) (pp 570–575). IEEE

Saw M, Saxena T, Kaithwas S, Yadav R, & Lal N (2020) Estimation of prediction for getting heart disease using logistic regression model of machine learning. In: 2020 international conference on computer communication and informatics (ICCCI) (pp 1–6). IEEE

Soni VD (2020) Chronic disease detection model using machine learning techniques. Int J Sci Technol Res 9(9):262–266

Indrakumari R, Poongodi T, Jena SR (2020) Heart disease prediction using exploratory data analysis. Procedia Comput Sci 173:130–139

Wu C S M, Badshah M, & Bhagwat V (2019) Heart disease prediction using data mining techniques. In: proceedings of the 2019 2nd international conference on data science and information technology (pp 7–11)

Tarawneh M, & Embarak O (2019) Hybrid approach for heart disease prediction using data mining techniques. In: advances in internet, data and web technologies: the 7th international conference on emerging internet, data and web technologies (EIDWT-2019) (pp 447–454). Springer International Publishing

Rahman AS, Shamrat FJM, Tasnim Z, Roy J, Hossain SA (2019) A comparative study on liver disease prediction using supervised machine learning algorithms. Int J Sci Technol Res 8(11):419–422

Gonsalves A H, Thabtah F, Mohammad R M A, & Singh G (2019) Prediction of coronary heart disease using machine learning: an experimental analysis. In: proceedings of the 2019 3rd international conference on deep learning technologies (pp 51–56)

Khan A, Uddin S, Srinivasan U (2019) Chronic disease prediction using administrative data and graph theory: the case of type 2 diabetes. Expert Syst Appl 136:230–241

Alanazi R (2022) Identification and prediction of chronic diseases using machine learning approach. J Healthcare Eng. https://doi.org/10.1155/2022/2826127

Gouda W, Almurafeh M, Humayun M, Jhanjhi NZ (2022) Detection of COVID-19 based on chest X-rays using deep learning. Healthcare 10(2):343

Kumar A, Satyanarayana Reddy S S, Mahommad G B, Khan B, & Sharma R (2022) Smart healthcare: disease prediction using the cuckoo-enabled deep classifier in IoT framework. Sci Progr. https://doi.org/10.1155/2022/2090681

Monday H N, Li J P, Nneji G U, James E C, Chikwendu I A, Ejiyi C J, & Mgbejime G T (2021) The capability of multi resolution analysis: a case study of COVID-19 diagnosis. In: 2021 4th international conference on pattern recognition and artificial intelligence (PRAI) (pp 236–242). IEEE

Al Rahhal MM, Bazi Y, Jomaa RM, Zuair M, Al Ajlan N (2021) Deep learning approach for COVID-19 detection in computed tomography images. Cmc-Comput Mater Continua 67(2):2093–2110

Men L, Ilk N, Tang X, Liu Y (2021) Multi-disease prediction using LSTM recurrent neural networks. Expert Syst Appl 177:114905

Ahmad U, Song H, Bilal A, Mahmood S, Alazab M, Jolfaei A & Saeed U (2021) A novel deep learning model to secure internet of things in healthcare. Mach Intell Big Data Anal Cybersec Appl 341–353

Mansour RF, El Amraoui A, Nouaouri I, Díaz VG, Gupta D, Kumar S (2021) Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems. IEEE Access 9:45137–45146

Sevi M, & Aydin İ (2020) COVID-19 detection using deep learning methods. In: 2020 international conference on data analytics for business and industry: way towards a sustainable economy (ICDABI) (pp 1–6). IEEE

Martinsson J, Schliep A, Eliasson B, Mogren O (2020) Blood glucose prediction with variance estimation using recurrent neural networks. J Healthc Inform Res 4:1–18

Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Xia Y (2020) Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Trans Med Imaging 40(3):879–890

Hemdan E E D, Shouman M A, & Karar M E (2020) Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055

Zhu T, Li K, Chen J, Herrero P, Georgiou P (2020) Dilated recurrent neural networks for glucose forecasting in type 1 diabetes. J Healthc Inform Res 4:308–324

Cheon S, Kim J, Lim J (2019) The use of deep learning to predict stroke patient mortality. Int J Environ Res Public Health 16(11):1876

Li K, Liu C, Zhu T, Herrero P, Georgiou P (2019) GluNet: a deep learning framework for accurate glucose forecasting. IEEE J Biomed Health Inform 24(2):414–423

Wang W, Tong M, Yu M (2020) Blood glucose prediction with VMD and LSTM optimized by improved particle swarm optimization. IEEE Access 8:217908–217916

Rashid N, Hossain M A F, Ali M, Sukanya M I, Mahmud T, & Fattah S A (2020) Transfer learning based method for COVID-19 detection from chest X-ray images. In: 2020 IEEE region 10 conference (TENCON) (pp 585–590). IEEE

Arora P, Kumar H, Panigrahi BK (2020) Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos, Solitons Fractals 139:110017

MathSciNet   Google Scholar  

Zaitcev A, Eissa MR, Hui Z, Good T, Elliott J, Benaissa M (2020) A deep neural network application for improved prediction of in type 1 diabetes. IEEE J Biomed Health Inform 24(10):2932–2941

Naz H, Ahuja S (2020) Deep learning approach for diabetes prediction using PIMA Indian dataset. J Diabetes Metab Disord 19:391–403

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Badawy, M., Ramadan, N. & Hefny, H.A. Healthcare predictive analytics using machine learning and deep learning techniques: a survey. Journal of Electrical Systems and Inf Technol 10 , 40 (2023). https://doi.org/10.1186/s43067-023-00108-y

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• Healthcare prediction
• Artificial intelligence (AI)
• Machine learning (ML)
• Deep learning (DL)
• Medical diagnosis

Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review

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• Chander Prabha   ORCID: orcid.org/0000-0002-2322-7289 42 ,
• Priya Mittal   ORCID: orcid.org/0000-0003-2171-0717 42 ,
• Kshitiz Gahlot   ORCID: orcid.org/0000-0001-9966-7574 42 &
• Vasudha Phul   ORCID: orcid.org/0000-0003-4066-4269 42  

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The artificial intelligence Internet of Things (AIoT), one of the most rising topics, has become a hot topic of conversation all the times. In recent years, it has been successful in attracting the attention of many people. Many researchers have focused now their research on AIoT; however, in health care, the research is still in the Stone Age. Smart Healthcare System is nothing new but introducing an innovative and more helpful version of traditional medical facilities, artificial Intelligence, IoT, and cloud computing which will surely take our healthcare system to new heights. This new technology is getting more important with increasing days as it is more convenient and more personalized for both doctors and of course for patients too. Role in the healthcare domain is also mandatory, as it focuses on intrabody monitoring services as well as maintains the healthcare records of patients. In the introductory part of this paper, the discussion is made on the great use of Smart Healthcare Systems by humans. In a brief literature survey, ideas about various techniques proposed in smart health care are presented. The later section expounds on various sensors used in smart health care and is of great benefit for old-age people. Sensors like nap monitors or breath monitors also help people to take care of their health and reduce the risk of any type of health issues in the future. The existing challenges with the use of AIoT in smart health care are discussed along with their applications favoring real-life examples used in the healthcare industry which are also part of this research paper.

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Google Scholar  

Priyadarshini S, Sahebzathi S, Narmatha M, R Delshi Howsalvya Devi (2016, July) The internet of things in healthcare: a survey. GRD J|Global Res Dev J Eng|Int Conf Innovations Eng Technol (ICIET). 559–564. e-ISSN: 2455-5703

Marr B (2018, May) How much data do we create every day? The mind-blowing stats everyone should read. Forbes

Sanghavi J (2019) Review of smart healthcare systems and applications for smart cities. In: ICCCE 2019. Springer Singapore

Magarino I-G, Sarkar D, Lacuesta R. Wearable technology and mobile applications for healthcare. 2019:6247094. published 21 May (2019)

Agarwal S, Prabha C (2021) Diseases prediction and diagnosis system for healthcare using IoT and machine learning , smart healthcare monitoring using IoT with 5G: challenges, directions, and future predictions. CRC Press, pp 197–228

Prabha C, Sharma G (2021, Dec) Applications of machine learning in cancer prediction and prognosis, cancer prediction for industrial IoT 4.0: a machine learning perspective. CRC Press (Taylor & Francis). eBook ISBN 9781003185604

Ganapathy K (2022) Drones in healthcare, issue 47. https://www.asianhhm.com/healthcare-management/drones-in-healthcare

Kaur G, Gupta M, Kumar R (2021) IOT-based smart healthcare monitoring system: a systematic review. 25(1):3721–3728

Kapoor A. Pulse oximeter buying guide: Price, how to buy the right oximeter types and more. https://timesofindia.indiatimes.com/most-searched-products/health-and-fitness/buying-guide/pulse-oximeter-buying-guide-features-price-how-to-buy-the-right-one-more/articleshow/79992362.cms . Accessed 13 Jan 2022

Fletcher J (2019, Oct) What is the difference between CT scans and MRI scans? https://www.medicalnewstoday.com/articles/326839

McIntosh J (2014) https://www.medicalnewstoday.com/articles/278847 , June 26

Zhang K, Ni J, Yang K, Liang X, Ren J, Shen X (2017) Security and privacy in smart city applications: challenges and solutions. IEEE Commun Mag 122–129

World Bank. Poverty and inequality platform. https://data.worldbank.org/indicator/SI.POV.NAHC

Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):1–19

Article   Google Scholar  

Nazir S, Ali Y, Ullah N, García-Magariño I (2019) Internet of things for healthcare using effects of mobile computing: a systematic literature review. Wirel Commun Mobile Comput. https://doi.org/10.1155/2019/5931315

Duan L-Y, Liu J, Yang W, Huang T, Gao W (2020) Video coding for machines: a paradigm of collaborative compression and intelligent analytics, arXiv preprint arXiv:2001.03569

Jing L, Tian Y (2020) Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell

He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 9729–9738

Yuan H, Bi J, Zhou M, Liu Q, Ammari AC (2021) Biobjective task scheduling for distributed green data centers. IEEE Trans Autom Sci Eng 18(2):731–742. Art no 8951255

Zhang J, Tao D (2020, Nov) Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J 20(10)

Islam SMR, Kwak D, Kabir H, Hossain M, Kwak K-S (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708

Yash KS, Sharma T, Tiwari P, Venugopal P, Ravikumar CV (2020, June) A smart wheelchair for health care systems. IJEET 11(4):22–29

Huh J-H, Choi J-H, Seo K (2021) Smart trash bin model design and future for smart city. 11(11):4810. https://doi.org/10.3390/app11114810

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Prabha, C., Mittal, P., Gahlot, K., Phul, V. (2023). Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review. In: Singh, Y., Verma, C., Zoltán, I., Chhabra, J.K., Singh, P.K. (eds) Proceedings of International Conference on Recent Innovations in Computing. ICRIC 2022. Lecture Notes in Electrical Engineering, vol 1011. Springer, Singapore. https://doi.org/10.1007/978-981-99-0601-7_23

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IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

Suliman abdulmalek.

1 Faculty of Electrical & Electronic Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Malaysia

2 Faculty of Engineering and Computing, University of Science & Technology, Aden 8916162, Yemen

Abdul Nasir

Waheb a. jabbar.

3 School of Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK

Mukarram A. M. Almuhaya

Anupam kumar bairagi.

4 Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh

Md. Al-Masrur Khan

5 Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea

Seong-Hoon Kee

Associated data.

Not applicable.

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

1. Introduction

The term Internet of Things (IoT) was invented by Kevin Ashton in 1999 and refers to data on the Internet that are connected to evolving global service architecture [ 1 , 2 ]. IoT is the product of advanced research on information and communications technology. It can potentially enhance urban residents’ quality of life. Since the global population is increasing at an astonishing rate, and the prevalence of chronic diseases is also on the rise, there is growing demand for designing cost-effective healthcare systems that can efficiently manage and provide a wide range of medical services while reducing overall expenses [ 3 , 4 , 5 , 6 ]. The IoT has become a key development area recently, enabling healthcare-monitoring system advancement. The IoT healthcare-monitoring system aims to accurately track people and connect various services and things in the world through the Internet to collect, share, monitor, store, and analyze the data generated by these things [ 7 ]. However, the IoT is a new paradigm where all connected physical objects in any intelligent application, such as smart city, smart home, and smart healthcare, are addressed and controlled remotely. Diagnosing disorders and monitoring patients is essential to providing medical care, and applying sensor networks to the human body will significantly assist in this endeavor. In addition, the information is readily accessible from any location in the world at any given time [ 8 ].

Patients with severe injuries or from certain areas may have difficulty reaching the hospital. Therefore, they can use video conferencing to communicate with their doctors to improve their health while saving money and time. Patients can use this technology to record their health conditions on their phones [ 9 ]. It is anticipated that the benefits of the IoT will be improved and result in individualized treatment, improving patient outcomes while saving healthcare management costs. IoT systems allow physicians to keep an eye on their patients remotely and schedule their appointments more efficiently. Patients also can improve their home healthcare to reduce their need for doctor visits and the likelihood of receiving unnecessary or inappropriate medical treatments in hospitals or clinics. For this reason, the quality of medical care and the overall safety of patients may improve, while the overall cost of care may decrease. The IoT holds significant potential in healthcare [ 7 , 10 ]. It will not be long before we have access to a health-monitoring system that can be used from the comfort of our homes and streamline hospital processes. IoT sensors should be densely deployed to monitor the body and environment continuously. This effort will enable the tracking of chronic-disease management and rehabilitation progress. In the future of virtual consultations for remote medical care, the IoT will be able to provide efficient data connections from multiple locations [ 11 ].

Most of the current implementations of the IoT and research on it are undeveloped and focus on deploying and configuring technology in various contexts and conditions. However, these practices are not widely used today. Therefore, this paper aims to evaluate related research on designing and implementing an IoT-based healthcare-monitoring system that improves quality of life. These systems rely heavily on IoT devices and sensors to connect patients with the healthcare providers best suited for their care.

The main contribution of this research paper is to highlight IoT-based healthcare-monitoring systems in detail so that future researchers, academicians, and scientists can easily find a roadmap to understand the current healthcare-monitoring systems and can easily provide solutions and enhancements for such critical applications. In this research paper, we provide a general idea of IoT-based healthcare-monitoring systems in a systematic way, along with their benefits and significance, and a literature review. Moreover, we discuss the concepts of wearable things in healthcare systems from an IoT perspective. The paper also provides a classification of healthcare-monitoring sensors, addresses security and protocols for IoT healthcare-monitoring systems, and details challenges and open issues. We also suggest solutions to overcome these challenges and issues in the future.

The paper is divided into eight sections as follows: Section 2 discusses the IoT-based healthcare system and its applications and the significance of using the IoT in the healthcare domain, followed by a review of the recent related studies in Section 3 . Section 4 describes the Internet of wearable things and wearable sensors in the healthcare-monitoring system; this section also provides a classification of heath-monitoring sensors. Section 5 emphasizes security and protocols for IoT healthcare-monitoring systems. Section 6 describes IoT healthcare challenges and open issues. Suggestions and recommendations are described in Section 7 , and Section 8 provides the conclusion of the overall review. Figure 1 shows the overall paper structure.

Research overview.

2. IoT-Based Healthcare Systems and Their Applications

IoT-based healthcare systems and their applications facilitates people’s lives in different ways, such as:

• Remote healthcare: Wireless IoT-driven solutions bring healthcare to patients rather than the patient to healthcare. Data are collected securely through IoT-based sensors, and the data are analyzed by a small algorithm before being shared with health professionals for appropriate recommendations.
• Real-time monitoring: IoT-driven non-invasive-monitoring sensors collect comprehensive psychological information. Gateways and cloud-based analysis manage the storage of data.
• Preventive care: IoT healthcare systems use sensor data, which help with the early detection of emergencies and alerts family members. Machine learning for health-trend tracking and early anomaly detection is achieved through the IoT approach [ 12 ].

2.1. The Significance of IoT-Based Healthcare-Monitoring Systems

The development of monitoring systems for healthcare is receiving a great deal of attention from researchers and leaders in the medical field. Several successful research projects have been conducted in this area, and many more are currently underway [ 13 ]. The number of gaps in care provided by healthcare providers is increasing significantly, directly resulting from the rapidly growing number of older adults and patients with chronic illnesses. The major shortcoming is that healthcare is only provided in hospitals; therefore, it is unsuitable for seniors and people with disabilities and cannot always meet their needs [ 14 ]. The IoT, with the help of sensor values and telecommunications, provides an effective and practical solution to the issue of real-time monitoring of the health status of the elderly. It has been shown that the IoT, in conjunction with smart technologies, can provide various improved and enhanced services. Using sensors, researchers have developed various emergency systems using technologies that enable intelligent and remote wireless communication. These technologies have been used for various medical purposes, particularly in monitoring the health of the elderly. This way, data can be collected on general health and dangerous situations by capturing important vital signs [ 15 ].

2.2. Benefits of Using IoT in Healthcare

The IoT will reshape healthcare as we know it, with profound implications. In terms of how apps, devices, and people communicate with each other to deliver healthcare solutions, we have reached a whole new level of evolution. The IoT has given us a new perspective and tools for an integrated healthcare network, greatly improving healthcare quality.

The IoT has made it possible to automate healthcare procedures that previously required a significant amount of time and left room for error due to human involvement. For example, to control airflow and temperature in operating rooms, many hospitals now use networked devices.

There are almost endless ways the IoT can improve medical care; however, the following are some of the key benefits:

• Reduced cost of care.
• Human errors are reduced.
• Elimination of the limitations of distance.
• Reduced amounts of paperwork and record keeping.
• Chronic diseases are detected early.
• Improvements in medication management.
• The need for prompt medical care.
• Better treatment outcomes.

3. Review of Recent Related Studies

Healthcare is a vast arena that is composed of many different components. Delivering healthcare involves clinical practices, hospitals, pharmacies, home health providers, long-term care providers, pharmaceutical companies, and medical-device manufacturers. It also involves health and wellness products and services, insurance companies, and governments providing services to end-users [ 6 ]. This section provides a review with an analysis of the recent research on IoT-based healthcare-monitoring systems. Table 1 summarizes some of the recent studies regarding IoT-based healthcare-monitoring systems.

The wearable device developed by Wu et al. [ 16 ] monitors various physiological parameters, including body temperature (BT), electrocardiograph (ECG), and heart rate (HR). Using Pulse Arrival Time (PAT) to measure ECG and PPG, it is possible to estimate blood pressure (BP). The interaction between humans and remote monitoring programs is straightforward because all the components are designed within a rigid framework. In addition, the power consumption of the devices is low, and they can communicate wirelessly to make tailored measurements of a specific physiological signal. The physiological measurements can be wirelessly transmitted to a gateway using a BLE module. The data are encrypted at the sensor patch and gateways to maintain privacy, ensuring transmission security. The wearable sensor system is connected to the cloud using a smartphone and a Raspberry Pi module as a gateway; the data can be retrieved and analyzed from the cloud. Despite its low energy consumption, BLE technology is unsuitable for wireless communication over long distances and high data rates.

Islam et al. [ 17 ] developed an intelligent monitoring system for use in a hospital. It not only collects data on patients’ BT, HR, and other vital signs but also monitors environmental factors in the hospital room, such as CO, CO 2 , and humidity. The success rate of modern healthcare systems is ~95% agreement between monitored and actual data in all cases. Medical staff can view the data in real-time, either on-site or remotely. Hypothetically, the technology would be helpful during medical crises and epidemics, as medical personnel would have almost instant access to raw data. The prototype created is incredibly easy to design and use. Such devices could be helpful in managing infectious-disease outbreaks, such as COVID-19. Potentially, this system could save more lives by improving the efficiency of the existing healthcare system. However, at this stage, the system still lacks some epidemic-related sensors that need to be evaluated once implemented.

In [ 18 ], Al-Sheik and Ameen propose an IoT health-monitoring system for cell phones that remotely monitors patients’ vital signs, including BT, ECG, and blood-oxygen saturation (SpO2). Arduino was used to measure and process this system. This system uses Wi-Fi to send the data to a cloud service on the IoT platform called Blynk; the data can be monitored in real-time. For security and privacy reasons, the results are sent to a specific smartphone that the doctor can monitor. Therefore, two microcontrollers, Arduino and NodeMCU, are used, which still need to be improved. For long-distance transmission, Wi-Fi technology is not the ideal option.

Hamim et al. [ 19 ] present an IoT-based healthcare-monitoring system for patients and older adults based on an Android application. The sensors in this prototype collect BT, HR, and Galvanic Skin Response (GSR) data that are fed into a single system, the Arduino Uno platform. Raspberry Pi transfers the data to cloud storage. Android Studio was used to develop the Android app, in which health parameters collected from patients can be visualized. Doctors can use the application to prescribe necessary prescriptions and track the patient’s health over time.

Using Raspberry Pi 3, Swaroop et al. [ 20 ] developed an IoT-based real-time health-monitoring system. Data creation, acquisition, processing, communication, and access are the main phases of the system structure. Health data such as HR, BT, and BP were measured. The data are transmitted through modes such as BLE, GSM, and Wi-Fi, i.e., mobile applications, messaging services, and the Internet. It was found that the latency is low, and there is no significant delay between sending and receiving data. Thus, the system’s accuracy is limited to the accuracy of the sensors.

Gupta outlines a healthcare-monitoring system using the IoT for obese patients [ 21 ]. The prototype is a fully functional device that measures body characteristics such as HR, SpO2, BP, and BT. This device is ideal for regular monitoring of body conditions. The system uses an Arduino board to store medical data for multiple patients simultaneously, and then, sends the information to healthcare providers via a Wi-Fi module for remote monitoring. Clinicians can use the recorded data to examine patients’ health patterns over time in order to detect any changes that may indicate an underlying, undetected health problem. Consequently, long-distance communication can be a challenge with this system.

To help physicians diagnose and monitor their patients’ health status, Alamsyah and Ikhlayel developed a monitoring system based on an IoT that can detect vital signs [ 22 ]. The system uses sensors to collect vital signs such as HR, BP, and BT. The data from the sensors are gathered and processed by Raspberry Pi before being uploaded to the cloud. The data can be retrieved remotely through a mobile app that allows easy access for medical staff. The results of retrieving vital-sign data show that the instrument was developed and the system was tested and evaluated reasonably.

An IoT-based real-time health-monitoring system can save a patient’s life by continuously monitoring the patient’s vital signs. The real-time health-monitoring solution proposed by Sangeethalakshmi et al. [ 23 ] continuously monitors patients wirelessly via a mobile app and GSM. Sensors capture vital signs that are transmitted to the cloud via Wi-Fi. The system consists of a data-acquisition module, a microcontroller (ESP32), and software. This system regularly measures and stores the patient’s BP, BT, ECG, HR, and SpO2 and transmits the data to the physician’s cell phone for analysis. The system also includes an alert system in which the physician’s cell phone receives a message when the patient’s vital signs are outside acceptable parameters. However, the system is only a prototype that still needs to be evaluated, tested, and calibrated.

Another IoT-based vital-sign-monitoring system is described in [ 24 ] by Sahu et al. Similar to other systems, vital signs are monitored in real-time, and the data that are collected are locally stored, and then, transferred to the cloud, from where they can be evaluated. The system detects abnormalities, sends alerts, and calculates early-warning scores. By storing the data on a personal server, the Android app reduces the burden placed on central medical servers and minimizes the server’s maintenance costs. The system is compact, portable, and easy for patients to use. Additionally, the system has been tested and evaluated against most other systems in the field.

A. D. Acharya and S. N. Patil designed and implemented an IoT-based smart medical kit for critical medical conditions [ 25 ]. This kit can provide a versatile connection to data from the IoT and can support emergency medical services such as intensive care units. The model collects, stores, analyzes, and distributes Big Data in real-time, enabling users to lower their health risks and reduce healthcare costs. This research aimed to reduce patient anxiety about regular doctor visits. With the help of this project proposal, patients’ and doctors’ time will be saved, allowing doctors to help patients in critical condition as much as possible.

Jennifer S. Raj [ 26 ] proposed a novel information-processing system for IoT-based healthcare-monitoring systems to manage Big Data in an IoT environment effectively. The entire data-processing process is divided into three stages: collection and aggregation, the classification and analysis of collected data, and decision-making. The experiments were conducted using Python. This model was experimentally verified in a simulation by using different health sensors. The parameters were compared with existing hierarchical clustering and backpropagation neural network models to validate the performance. This model leverages Apache Kafka and Hadoop to address the need for real-time data collection and offline processing. According to the authors of this study, the proposed method outperforms the more traditional hierarchical clustering model and the backpropagation neural network model in data processing and information extraction; they claim that their proposed model achieves 97% accuracy. The study does not provide a comparative analysis of time-efficiency for the model.

Kishor and Chakraborty designed a healthcare model using seven classifying algorithms [ 27 ]. Nine different disease related datasets were organized based on classifications. AUC, accuracy, sensitivity, and specificity were the four variables used to measure the classifiers’ performance. The three phases of this work were data collection, pre-processing and computation, and determining the results’ visibility to physicians or end-users, with the results stored on a cloud server. This study compared machine learning-based health models with previously developed work. Unlike other classifiers, the RF classifier has the highest accuracy, sensitivity, specificity, and AUC for a variety of common diseases, according to the study authors. This model can be extended for various purposes, such as weather forecasting, military, and food availability prediction.

In another study that is very similar to [ 27 ], Souri et al. suggested an IoT-based system for monitoring student health [ 28 ]. This model aimed to monitor students’ valuable metrics and identify behavioral and biological changes in students using cutting-edge student-support technologies. This approach consists of three levels: identifying the required data for the student using biological and behavioral factors, capturing the information using biosensors and intelligent IoT devices, and pre-processing the data. In this process, four classifiers were employed to assess the validity of the proposed model. The experiment results showed that the classification algorithms performed superbly in terms of precision, recall, accuracy, and F-score. The authors stated that SVM achieved the highest possible performance in predicting diseases in the proposed scenario. This system requires a local repository to reduce the time needed for emergency services, which saves bandwidth within the system. The response time of this system is not fast enough.

A healthcare system based on a Random Forest Classifier and the IoT was proposed by Kaur et al. [ 29 ] to improve interactions between patients and healthcare professionals. The experimental results were compiled using eight datasets on different diseases to determine whether the proposed work is successful or not. Five different machine learning approaches were used in this work. According to the authors, the Random Forest learning technique achieved a maximum accuracy of 97.26% when applied to the dermatology dataset. In addition, it was claimed that Random Forest provided good and accurate results for each dataset considered. Accuracy and area under the curve (AUC) were the two-performance metrics used for different machine learning techniques and datasets, respectively.

Gera et al. [ 6 ] concentrated on an IoT-based Cloud Talk platform-connected patient-health-monitoring system. This system streamlines the conventional workflow by providing all systems—including medical examinations, facilities, and tests—in one location. This system is capable of being implemented in a real-world setting because it consists of five fundamental components that are able to carry out a variety of tasks, such as collecting patient data from wearable IoT sensors, uploading the report to a cloud platform, analyzing the findings, and providing medical check-ups, diagnostics, and facilities to patients. In addition to these benefits, the system facilitates better decision-making and makes navigating the conventional workflow of the normal healthcare system simpler. In addition, it acts as a point of contact for the patient, the doctor, the pharmacist, and the diagnostician. There are restrictions on the system’s ability to manage patient healthcare.

SoonHyeong et al. [ 30 ] proposed an intelligent health-related monitoring system that detects abnormal movements such as falls based on sensor readings from accelerometers. After detecting abnormal movements, the system analyzes basic bio-signals such as a person’s BP, HR, and BT. Users, caregivers, and professionals can check that the patient has measured biometric data anytime, anywhere, using a smartphone. This monitoring system includes a JAVA-based Android service environment. The performance of this monitoring system was evaluated using datasets with information from fifty different individuals. In this model, blockchain technology is used to protect individuals’ medical data by increasing the data’s reliability while maintaining its confidentiality. With the help of a sensor chip, technology that is part of the IoT, the accumulation of personal medical information is stored and monitored in real-time. The transmission of sensitive medical data occurs in real-time via a mobile device only, such as a smartphone.

Piyush et al. [ 31 ] present a positive strategy for monitoring the daily life of Alzheimer’s patients and providing quality care to those affected by the disease. This work is based on data collected from sensors connected to the IoT that determine various parameters of the patient’s body, such as temperature, BP, pacing, and walking speed, to name a few. The Atmega microcontroller is used for collecting all this sensory data and information. All the collected information is transmitted to a cloud server using parallel communication to analyze the data. It is possible to retrieve the patient’s desired parameters, which helps provide real-time patient support. In addition, this work cannot predict the patient’s condition before the emergency becomes more serious.

An IoT-based healthcare-monitoring system with numerous sensors and an intelligent security system was presented by Hashim et al. in [ 32 ]. The system uses many sensors to collect vital signs such as humidity and room temperature using a DHT11 sensor, HR using a pulse sensor, and BT using an infrared thermometer. Data from the sensors that used the Arduino to gather information on the condition of the patient are sent to ThingSpeak and stored using the Wi-Fi module. The collected data are displayed on the LCD (cloud platform). When the sensor detects an abnormal reading, an SMS is sent to the smartphone using a GSM module to contact the patient’s family or doctor promptly. The performance of the temperature and pulse sensors was evaluated using various experiments. According to the authors, the percentage error of the infrared thermometer sensor is 1.2% lower than that of the current model. The user and physician can view the results when uploaded to ThingSpeak, but this system cannot monitor the patient remotely in real-time.

A platform for IoT-based health monitoring was proposed by Mostafa et al. [ 33 ] that uses a NodeMCU microcontroller to obtain readings from a DS18B20 temperature sensor and a Max30100 pulse oximeter to determine BT, HR, and SpO2 values. The readings are displayed on an LCD in front of the patient and on the Blynk app-enabled phones of the physician and everyone else involved. This project also included an infrared sensor (IR) that detects objects in front of it and activates a relay to pump disinfectant without being touched. According to the authors, the application takes only one minute, and their project works flawlessly compared to the conventional method. NodeMCU, a less-expensive and -complicated processor with built-in Wi-Fi, is used in this system, making it more cost-effective than other existing systems. Although the system is only for cardiac patients, it surpasses the traditional systems by providing a safer, easier, faster, and more affordable service.

A Wi-Fi-connected smartphone and an electronic wearable device ere used by Jenifer et al. [ 34 ] as part of an IoT-based health-monitoring system. This system uses sensors to measure the patient’s physiological parameters, including HR, BT, BP, and SpO2. The patient’s data are collected via Wi-Fi from a remote location and stored in a cloud server, and the health parameters are continuously monitored. If abnormalities occur, an automatic alert is sent to medical professionals with the patient’s location. However, this study does not include experimental data or a comparative analysis.

Dhruba et al. [ 35 ] use the IoT to monitor sleep apnea. They developed a simple application using a basic microcontroller and a selection of key health-related sensors. After analyzing data from five different people, the system provided results that were quite suitable for determining whether or not someone is suffering from sleep apnea. According to the authors, two people did not have any sleep apnea symptoms, although they had been diagnosed. The individuals in question were between 36 and 50 years old and had significant problems with their sleep patterns. The system successfully detected the presence of sleep apnea in these individuals. This system can also detect obstructive sleep apnea when a person is screened. In addition, a person with OSA is considered a patient if he or she is 50 years of age or older. With the help of this type of monitoring, people can detect sleep apnea at an earlier stage. People can learn more about sleep apnea and its detection with the help of this system. It will also help people solve any problems related to their ability to sleep. However, when the patient is sleeping, the worn devices may come off and cause an uncomfortable feeling.

Kshirsagar et al. [ 36 ] suggest an ongoing, low-effort electronic saline-monitoring and -control system that can automatically keep track of the rate of saline flow, the amount of time left, and the rate of infusion. This system can send data to the server from a distance and show the results, such as the saline bead rate, the condition of the failure, and how much time is left to empty the saline bottle, on the main screen. It can also show the volume of the mixture. However, this system only entertains a single purpose (electronic saline observation), and the contribution does not match the research title.

The research conducted by Tiwari et al. [ 37 ] focuses on developing an IoT-based remote monitoring system for healthcare using NodeMCU and the Arduino IDE. Ubidots is the IoT platform discussed in this article. The open-source IoT application is required for Ubidots to function correctly. It is also an application programming interface (API) that allows users to shop and retrieve data via HTTP and MQTT protocols while connected to the Internet or a local network. With this IoT device, it is possible to read pulse rate and measure temperature and BP. This configuration allows for round-the-clock monitoring of a patient’s vital signs and detects abnormalities that may be present. The results of the ECG test showed that the subjects’ average HRs were 72, 75, and 78 beats per minute, respectively. The recorded SpO2 percentages were as follows: 94, 97, and 98%, respectively. Finally, the participants in the experiment had a temperature of 94.78, 95.6, and 97.4 degrees Fahrenheit, respectively, when their temperature was taken. The authors noted that the design concept is simple and inexpensive to implement, considering its cost-effectiveness. On the other hand, transpiration could affect the design.

In order to monitor a person’s temperature, BP, HR, and SpO2, Vaneeta et al. [ 38 ] built a system based on the IoT. The ability of nearby clinics to communicate with city hospitals about their patients’ medical conditions makes this a valuable system for rural areas and small towns. The IoT system can alert the doctor or physician in case of any deviations from the standard values in the patient’s health. The maximum relative errors (percentage r) in the HR measurements, patient BT, and SPO2 were discovered to be 2.89%, 3.03%, and 1.05%, respectively. These values are comparable to the commercial health-monitoring system. This IoT-based health-monitoring device makes it effortless for physicians to collect real-time data. The system can monitor the parameters regularly because high-speed Internet is accessible. Additionally, the cloud platform enables data archiving, so those earlier measurements may be retrieved quickly. This system would make it possible to diagnose and treat COVID-19-specific patients early on.

Khan et al. [ 39 ] built an IoT-based health-monitoring system utilizing Arduino to measure a patient’s BT, HR, and SpO2. The data were then transmitted to an app using Bluetooth. The patient can gain a quick understanding of their current health status thanks to the information that is also transmitted to the LCD panel. COVID-19 patients, older adults patients, asthma patients, COPD patients, patients with chronic diseases, and diabetic patients can keep their condition under control with the assistance of this system over time.

The authors of [ 40 ] presented an ECG monitoring system for cardiac patients based on the IoT. The system is comprised of many components, including an ECG sensing network (data gathering), an IoT cloud (data transmission), and estimation results (data prediction). Based on the system, the authors operated Arduino Mega to process the received patients’ data and transfer them to the cloud using the Wi-Fi module in this system, ESP 8266. ECG information was stored in the cloud and was accessible using MQTT and HTTP servers. The linear regression approach determined the relationship between the properties of the ECG signal and the error rate. A prediction was performed to determine how much of a difference there was in PQRST regularity and whether it was enough for an ECG monitoring device. Acceptable outcomes have been attained by recognizing the quality-parameter values.

In this paper, we have discussed various healthcare-monitoring systems that are based on the IoT. These systems are very beneficial for both patients and medical professionals. Arduino, Raspberry Pi, NodeMCU, and Atmega are the four main embedded systems used to develop most existing health-monitoring systems. These embedded systems monitor patients’ health in real-time to ensure they receive the appropriate treatment on time. There are several research holes in the currently available IoT-based healthcare-monitoring systems. Most of the recent healthcare systems monitor HR, HR, SpO2, and BP. However, many other significant factors have not been considered, such as physiological, therapeutic, behavioral, and rehabilitation-related factors.

Summary of the existing healthcare-monitoring systems based on IoT.

Note: Aims and Contributions refers to the aim of the research work. Methodology refers to methods and techniques used. Hardware/Software Technology refers to hardware and software used. Features refers to main features addressed. Evaluation Metrics refers to evaluation metrics. Protocol refers to protocol utilized. Limitations refers to drawbacks of research works.

This may be another serious and urgent situation that a sophisticated health-monitoring system needs to consider, ensuring that prompt assistance and medical support are provided. Because the monitoring is performed in real-time, specific existing systems are deficient in their ability to aggregate data from the monitoring device. These data should be stored in the cloud to be analyzed later to determine whether or not an emergency exists in a patient’s profile. The differences and similarities between various IoT-based health-monitoring systems are outlined in Table 1 .

The gaps in the existing system can be summarized as follows:

• The IoT has the potential to be integrated with a wide variety of devices, which is not possible with most of the systems that are currently in use.
• There is the possibility that the data that are stored will not be protected.
• Complex systems have many disconnects between the various people, stages, and procedures.
• An investigation into the circumstances surrounding an accident will typically reveal the existence of several gaps, but gaps themselves are rarely the cause of accidents.
• The ability to understand and reinforce the normal ability of practitioners in order to bridge gaps contributes to an increase in overall safety.
• The conventional viewpoint, which maintains that systems ought to be shielded from the unreliable influence of humans, is challenged by this point of view.
• We have a limited understanding of how professionals pinpoint newly formed gaps and devise solutions to close them when systems undergo transformation.

4. Internet of Wearable Things

The Internet of Wearable Things (IoWT) aims to improve people’s quality of daily life. It involves sensors fitted into wearable devices, monitoring the individual’s activity, health factors, and other things. The data collected from the IoWT can be fed into medical infrastructure, giving clinicians remote access to their patients’ data as they go about their daily lives. Building on the IoT architecture, a novel integrative framework for IoWT is currently being developed. The IoWT is a revolutionary technology that has the potential to change the healthcare industry by creating an ecosystem for automated telehealth treatments [ 41 ].

As shown in Figure 2 , the architecture of the IoWT and its connections consists of three elements: the WBAN, the gateway connected to the Internet, and the cloud. The WBAN is a front-end component of IoWT that wraps around the body to collect health-related data unnoticed. The WBAN collects data from sensors in direct contact with the body or from sensors in the environment that can collect indirect data about a person’s behavior. The WBAN can either analyze the data or transmit them for remote analysis. In addition, mobile computing devices such as smartphones, tablets, and laptops must be connected to the Internet to send data to powerful computing resources [ 42 ].

Architectural elements of IoWT [ 42 ].

4.1. Wireless Network Technologies for IoT Healthcare

Healthcare systems can be monitored remotely using various wireless network technologies. The existence and operation of IoT emerging technologies, such as RFID, wireless network technologies (BLE, Wi-Fi, Zigbee), and low-power wireless area network (LPWAN) technologies (such as LoRa and SigFox) are engaging in terms of the IoT’s long-term development and deployment. They enhance device connectivity to the Internet, and the efficiency of IoT application operation [ 43 ].

BLE, LoRa, and Zigbee are wireless sensor network technologies; meanwhile, to identify and trace products, RFID is used. BLE can transfer data between different mobile devices [ 44 ]. Communication methods can be long in their range (LoRa, SigFox, and Wi-Fi) or short-range (Bluetooth, RFID, and Zigbee) [ 24 ]. Due to new communication protocols being created exclusively for IoT devices, such as LoraWAN, NB-IoT, and Sigfox, it is anticipated that the popularity of these applications will increase, enabling a far-reaching remote monitoring system [ 11 , 45 ].

An essential component of the IoT is the WSN. The IoT, which has already been established, can connect things to the Internet, allowing humans to interact with computers and for computers to interact with other computers. Thus, the combination of the IoT and WSN facilitates machine-to-machine communication. Figure 3 illustrates the architecture of IoT with the WSN. It shows sensor nodes communicating with a gateway in a separate network. Many devices are linked to the gateway via Wi-Fi or the Internet, ensuring interoperability [ 46 ].

Relationship of WSN to IoT [ 46 ].

The researchers in [ 24 ] counted the existing wireless applications in connected healthcare facilities to study operational wireless methods for transmitting data across short distances. The system design and implementation of family mobile medical care are presented in this study. The Android mobile client, data transmission, and a system server are part of the system. Wireless data transfer is potentially possible, at least in theory. An example of the mobile healthcare system’s success is shown here. In the first place, family members’ sign characteristics might be collected via sensors on medical equipment. ECG, BP, SpO2, respiration, and sleep are parameters of interest. The mobile terminal uploads data to a back-end Web server with a wireless network, Bluetooth, and Wi-Fi. Data storage, computation, and analysis are all handled by the MySQL database server [ 24 ]. A family member’s smartphone or tablet may be used to show data icons or text, making it easy for them to monitor their loved one’s health at any time and location. Family members may prevent significant health issues through early intervention, encouragement, and healthcare maintenance.

4.2. Wearable Sensors in Healthcare-Monitoring Systems

In real-time, the healthcare sector may use wearable devices to monitor and save patients’ activity and physiological functions. Such devices have one or more sensor nodes, but each sensor node typically has a radio transceiver, a low-speed processing unit, and small memory. The sensors can measure various physiological parameters and activity, including SpO2, BP and temperature, electrodermal activity (EDA), ECG, electromyography, HR, and RR [ 2 , 47 ].

Bluetooth, infrared, near-field communication (NFC), RFID, Wi-Fi, and Zigbee wireless transceiver technologies can support wearable devices communicating with smartphones and other devices. The technology promotes care by facilitating remote diagnosis and monitoring [ 11 ]. An important issue of discussion in this period revolves around the IoT in healthcare. One of the essential parts of healthcare is identifying and treating illness. In order to achieve this, the body sensor network will be valuable. Additionally, the data may be accessible from any location in the world [ 8 ].

A wearable sensor gadget created by Vedaei can monitor and analyze the actions of patients. An IoT technology that measures social distance might help prevent a COVID-19 sufferer from becoming sick. Three layers of IoT sensors, machine learning algorithms, and smartphone apps are used to monitor BP, SpO2, cough rate, and temperature daily. The frameworks outlined by the authors helped the users keep a safe distance between themselves and the transmission of the virus and update their information often. A distance-monitoring system based on Radio Frequency (RF) was also presented in the research, which may be used in both indoor and outdoor contexts. In order to compare the findings under environmental restrictions, the authors looked at two alternative situations. Those who wrote the article claim to have helped expose COVID-19 [ 48 ].

Another study [ 49 ] demonstrated an IoT-connected wearable sensor network system for industrial outdoor workplace health and safety applications. Wearable sensors worn by the worker collect physiological and environmental data, which are transferred to the system operator and employees for monitoring and analysis. Data harvested from multiple workers wearing wearable sensors can be transferred through a LoRa network to a gateway. The LoRa network combines a Bluetooth-based medical signal-detecting network with a heterogeneous IoT platform. The authors describe the sensor node hardware and design, the gateway, and the cloud application. A heterogeneous wearable IoT device sensor network system for health and safety usage is shown in Figure 4 .

Healthcare-monitoring system using wearable sensor [ 49 ].

4.2.1. Use Cases of Health-Monitoring Sensors

Medical science research is currently dominated by medical healthcare, which mostly relies on how it integrates with the IoT. This integration is receiving a lot of attention due to its crucial role in utilizing technological paradigms to save human lives. These integrated systems contain three crucial phases, namely, the modules for data collection, data processing, and data evaluation. Healthcare monitoring plays a significant role in the data collection module due to its active involvement in gathering data from various sources and specimens. Most healthcare-monitoring systems use sensors to obtain the necessary input data. The more concise and timely the data, the more accurate the results.

Sensors are employed for more than just data collection; they can also be used for various ongoing and post-monitoring tasks in IoT-based healthcare systems. Blood pressure, body temperature, pulse oximetry, and blood glucose are a few examples of heterogeneous wearable sensing devices developed to collect patients’ biomedical data [ 50 ] in the era of fast-growing IoT. The proper quality and development of these IoT-based healthcare-monitoring systems are directly related to reliable data from sensors or sensor networks, which necessitates using advanced signal-processing techniques, sensor data fusion, and data analytics. In medical science, sensors that measure heart rate, body temperature, and other things are used to find and diagnose diseases at the earliest stage.

It has been observed that health-monitoring sensors are utilized in various use cases of medical science for healthcare purposes, such as the monitoring of hemoglobin concentration, molecular diagnostics, clinical diagnosis of albumin-related diseases, heart-rate detection, blood-oxygen-saturation detection, respiratory-rate detection, anemia detection, Alzheimer’s disease, and many more.

There are many applications for wearable sensors. IoT-assisted wearables are widely used these days. The friendliness of such devices has created a boom in their application in all fields. With the healthcare field being no exception, the IoT’s exploits in healthcare are enormous. Various technologies are linked to existing technology that helps generate data for monitoring and analysis.

We have seen a lot of use cases for IoT-based sensors in real-time environments, which are mentioned below:

Use Cases/Applications

• Heart-rate detection/Cardiac monitoring systems/Stroke

The first application of health-monitoring sensors was through IoT-based healthcare-monitoring systems; these can gather and measure the necessary data, transmit these data through various stages reliably to the gateway and the cloud server, and perform some edge tasks to provide low-latency decision-making for cardiac-related diseases and prediction. Some of the pieces utilize sensors to determine heart rate [ 51 ]. Several projects involve using WSN technology to continuously monitor heart patients who need a real-time monitoring system [ 52 ]. This WSN has several medical-grade sensors and devices that can track blood pressure, body temperature, heart rate, and pulse. A critical patient’s real-time ECG is also preserved so that the patient is continuously watched [ 52 , 53 , 54 , 55 , 56 ].

• 2. Body-temperature measuring

During the pandemic, IoT-based smart health-monitoring devices with sensors for COVID-19 patients based on body temperature, pulse, and SpO2 were beneficial. Through a mobile application, these systems can measure a human’s body temperature, oxygen saturation, and pulse rate [ 57 ].

• 3. Activity recognition

One of the many uses for medical wearables now being used is activity recognition. Almost all fitness trackers perform this kind of recognition. Fitness trackers are now the most popular wearables for tracking a person’s activity. A lot of guesswork is being carried out in the background, but most of them include a highly sensitive 3D accelerometer that allows the sensor to determine the acceleration [ 51 ].

• 4. Blood-glucose monitoring and hemoglobin concentration

Heart-rate sensors, blood-glucose monitors, endoscopic capsules, and other devices make up the Internet of Medical Things (IoMT), which together, create the IoMT diabetic-based WBSN monitoring system [ 58 , 59 ].

• 5. Respiration-rate detection and monitoring

We can keep an eye on the human body’s respiratory system in several ways. Some writers employed sophisticated sensors that keep track of breathing patterns. A bio-impedance sensor can be useful [ 51 , 60 , 61 ].

• 6. Sleep monitoring

This sleep-tracking app assists the user in adjusting their sleep patterns and maintaining a healthy life cycle. For this, various sensors are utilized. Wearables often track heart rate, pulse rate, SpO2 levels, and breathing patterns, and by taking these measurements into account, they may make an educated decision regarding the quality of sleep [ 62 ].

• 7. Alzheimer’s disease monitoring and Anemia detection

Monitoring for Alzheimer’s disease has several issues and needs to be handled carefully. When a patient is alone, diagnosing them with Alzheimer’s is impossible [ 63 , 64 , 65 , 66 ].

• 8. Molecular diagnostics and Clinical diagnosis

Due to quick and affordable healthcare applications with reduced risk of infection, recent developments in biosensors for patient-friendly diagnosis and implantable devices for patient-friendly therapy have attracted a lot of attention. The rapid development of point-of-care (POC) sensor platforms and implantable devices with specialized functionality has been made possible by incorporating recently created materials into medical equipment [ 67 , 68 ]. A lot of work has been conducted on the clinical diagnosis of albumin-related diseases [ 69 , 70 ].

• 9. Blood-oxygen-saturation detection

Along with precise, ongoing monitoring of intravascular oxygen levels, it is crucial to monitor patients’ cardiovascular health following cardiothoracic surgery [ 71 ]. There are new types of data, such as oxygen saturation, which are continuously collected using oxygen-saturation (SpO2) sensors and represent the percentage of oxygen-saturated hemoglobin compared to the total amount of hemoglobin in the blood; these are becoming available for market wearables. Other behavioral and physiological biometric types are already available in many market wearables [ 72 , 73 , 74 ].

Thus, it has been shown that health-monitoring sensors are used in various applications and can be used in the future for various diseases, particularly those that focus more on sample or data collection, monitoring, or evaluation. We may assert that whenever a sensor is employed, there is a possibility to collect the necessary data and deliver the desired outcomes, depending on precision and accuracy. Additionally, incorporating the cloud, geographic information systems, and mobile devices has improved the process of sensor-based data gathering and monitoring while allowing for flexible remote sharing and communication.

Numerous case studies and applications are possible for health-monitoring sensors. They can be used to measure hemoglobin concentration; for molecular diagnostics; to provide clinical diagnoses of disorders associated with albumin; to measure heart rate, blood-oxygen saturation, respiration rate, and anemia; to diagnose Alzheimer’s; and for many other things.

4.2.2. Classification of Health-Monitoring Sensors

With advancements in wireless communications, medical sensor technology, and data-collection methods, it is now possible to remotely monitor a person’s health by putting wearable technology on them and analyzing the data collected. These sensors and wearable devices can be integrated into various accessories such as clothing, wristbands, glasses, socks, hats, and shoes, as well as other devices such as smartphones, headphones, and wristwatches.

Pawan Singh [ 75 ] classified medical sensors into two categories: contact sensors (i.e., on-body or wearables) and non-contact sensors (i.e., peripherals). Contact sensors are further classified into two sub-categories: monitoring and therapeutic. Again, non-contact sensors are further classified into three sub-categories. All the sub-categories are further classified based on their use. Figure 5 illustrates the classification of health-monitoring sensors with examples of their use.

Classification of health-monitoring sensors [ 75 ].

Primarily, health-monitoring sensors can be divided into contact (i.e., on-body) and non-contact (i.e., peripheral) sensors. Contact sensors are attached to the body to monitor physiological behaviors, chemical-level identification, and optical measurement-related monitoring. Contact sensors are also used in therapy-related monitoring such as medication, stimulation, and emergencies. Non-contact sensors are used for monitoring fitness- and wellness-related factors, behavior, and rehabilitation. An example of each type of monitoring is shown in Figure 5 .

The following are some of the medical applications that could benefit from the use of medical sensors and wearable devices [ 76 ]:

• Monitoring vital signs in hospitals.
• Aging in place and in motion.
• Assistance with motor and sensory impairments.
• Large-scale medical and behavioral research in the field.

Based on the applications in which they are most frequently used, we have divided health-monitoring sensors into different groups for performance-wise evaluations. These sensors can be divided into many categories, which are covered in the subsections. Figure 6 is a collection of several wearable sensors applied in various research projects and employed in IoT systems in healthcare.

Various application of use cases and IoT sensors for healthcare monitoring [ 62 , 77 , 78 , 79 , 80 , 81 , 82 ].

4.2.3. Performance Evaluation of IoT Sensors

Any healthcare-monitoring system’s sensors serve as its brain and heart. Thus, they must be reliable. Almost all types of sensors used should be small, quiet, accurate, have short data-transmission delays, use little power, and perform well overall. Wearable sensors must be both precise and compact, which presents a challenge. However, in case of wearable sensors, the more value is given to outputs, and they need to be reasonably accurate, too, so that the doctor can use these values to make decisions. Medical-grade sensors are large and difficult to transport and require specialized equipment and trained personnel [ 51 ].

Additionally, various IoT sensor-based applications constantly require authentication, security, and privacy. Numerous protocols are readily available on the market to assist with security and help offer some solutions over an extended period. Nevertheless, these integrated and crucial data-based apps’ security measures are constantly vulnerable to intrusion.

5. Security and Protocols for IoT Healthcare-Monitoring Systems

Along with the utilization of the IoT, there has also been an increase in the risk of new security assaults and weaknesses in healthcare systems. Healthcare data are highly sensitive and contain personal identifying information such as social security numbers. This is because many medical devices collect and share critical and sensitive patient-related data on the Internet via various connected devices for further evaluations and decisions. IoT technology’s nature presents complexity and incompatibility difficulties in medical-related IoT devices [ 83 ]. As a result, security issues such as a lack of availability, confidentiality, and integrity arise. Some of the IoT healthcare solutions include software and hardware that monitor and regulate patients’ vital signs in the form of monitoring services, which are connected to the IoT for data processing. However, these solutions are always at a high risk of security threats such as authorization, privacy, and authentication breaches [ 84 ]. Cybersecurity in healthcare has emerged as a big problem. Device flaws could be exploited by hackers, resulting in IoT system operational disruption. More importantly, due to the limitations of medical equipment, such as their scalability, power consumption, and interoperability, standard security criteria for countermeasures for attacks are not relevant. Moreover, when it comes to criteria for security, privacy, and dependability, the medical IoT technology should be trusted too. Additionally, some physical and technical protections to prevent data leakage are available on the market. However, these measures have fallen short of what is needed; stronger and more modern security standards should be implemented, and a resilient strategy should be implemented to save the crucial data [ 85 , 86 ]. Therefore, to better understand and develop a secure IoT-based healthcare infrastructure, it is necessary to also determine security requirements [ 87 ].

The available solutions could include more secure overlay networks such as the Onion Router (TOR) network, which might be used to transfer confidential data. Moreover, authentication and identity-verification methods such as signatures, voice patterns, finger-print scanning, passwords, and smart cards could be employed in application protocols. Existing security solutions, such as RSA, seed phrases, and DSS, may also be used at all connection endpoints. Technologies such as SDN, blockchain, and NFT tickets could be used to provide authentic and customized service. Last but not the least, artificial intelligence-based approaches that can be used to detect anomalies in IoT networks [ 87 , 88 ] could be implemented to overcome the issues and challenges of security in IoT-based healthcare-monitoring systems.

Eventually, with the advancements in the IoT’s common standards, many protocols have been created to evaluate the services that are used for IoT solutions, and their relevance, to connect a variety of devices to the Internet and various architectures. IoT protocols for a particular application are selected considering the application’s requirements [ 89 , 90 ]. Wearable technology, smart medical equipment, smart homes, and remote monitoring are some of the IoT’s most exciting healthcare applications. Some recent studies emphasized IoT interoperability, which includes the healthcare-domain aspects of the IoT, which should compulsorily include the standardization of dependable communication protocols for improved and enhanced mobile and wearable technology. In addition, low-cost, low-power embedded processors are useful solutions. The most popular emerging IoT communication protocols that are extensively used to develop smart IoT applications include CoAP, MQTT, XMPP, AMQP, DDS, LoWPAN, BLE, and Zigbee. The most promising IoT-based healthcare apps for patient monitoring, therapy, and diagnosis are dependent on these protocols. The main uses of these protocols are to enhance the performance of telehealth, medication management, chronic-disease detection, bio-physical parameter monitoring, home and eldercare, and chronic-disease monitoring [ 88 ].

6. IoT Healthcare Challenges and Open Issues

Although the IoT can provide personal health benefits, building data-collecting schemes that are efficient and secure to use in IoT healthcare-monitoring systems still presents numerous limiting issues. These various open research challenges, which include functionality, performance, data privacy, reliability, security, and stability, are considered in this section. We have divided the challenges and open issues into various categories: security-based, performance-based, computational-intelligence-based, integration-based, energy-based, and disease-prediction-based (see Figure 7 ).

IoT healthcare challenges and open issues.

6.1. Security-Based: Security and Privacy

There are ethical challenges to privacy and security. Hackers can easily access medical records, which are transformed into digital records (stored in electronic health records) and stored in the cloud. In a security breach of the cloud server, hackers can access patients’ medical data. This causes problems with user authentication, data ownership, data-protection policies, and misuse of health information [ 21 , 30 , 91 ].

Security and privacy must be addressed in IoT system design and development to improve confidence in employing the IoT in healthcare. Each IoT layer and component must have security protocols to reduce security risks and protect privacy. Developers must ensure that IoT “things” and the systems they connect to are secure, so that users can rely on sensors, devices, gateways, and IoT services, and so that their identity, safety, and privacy are protected. Numerous commercial and personal technologies are built without ensuring these security and privacy factors [ 92 ]. Various health IoT-based remote monitoring applications are majorly influenced by the integrated security mechanisms with built in hardware and software components of sensors and transceivers for wireless communications. Applications that collect data from these sensors and devices are typically designed with privacy in mind, using strong authentication and encryption measures and other safeguards, during both storage and transmission. Commonly, these apps integrate with pre-existing healthcare information providers, whose own security measures and privacy policies are put into effect. It is still possible that they have not adopted the most recent measures to secure data [ 11 , 93 ].

Solutions aimed at protecting individuals’ privacy should give people the power to choose who can lawfully view and make changes to their data. Users of the IoT need to trust that their personal information will be handled securely and responsibly. Multiple laws and policies, such as HIPAA and the EU’s General Data Protection Regulation, have already addressed privacy concerns when creating IoT applications (GDPR). There is, nevertheless, a requirement to think about the secondary use of the data gathered via home IoT remote monitoring. Patients using these systems may provide their permission for their information to be used just for the home health-monitoring system [ 11 , 94 ].

Indeed, securing data and assuring privacy remains a key challenge to the health IoT. Data that are transmitted to the data-processing unit could be spied upon, or the data could be manipulated, leading to a flawed analysis of Big Data. Therefore, ensuring the data are transmitted securely from the nodes to the processing unit is critical. Furthermore, during data processing, the identity of the individual yielding the data must be protected. By adopting cryptographic methods, the algorithms that process the data do not need to map the data to the user [ 91 , 95 , 96 ].

6.2. QoS-Based: Performance, Fuctional Stability and Reliability, and Cost

The priority of Quality of Service (QoS) is not consistent but varies depending upon needs; it safeguards a particular level of data-transmission performance. The primary challenge is maintaining the integrity of sensitive patient data while exchanging data from the end node to the server node. In the IoT, latency is the duration needed to send a packet of data between node devices [ 97 ].

QoS indicators apply to all the IoT architecture sub-components, from the home of the individual to the healthcare cloud services. Memory consumption needs to be checked to ensure there is no leakage of memory or data being cached inappropriately. Delays and interruptions to data transfer due to wireless disruption result in unexpected disconnection or erratic connectivity, poor signal, and slow network speeds. Another performance metric is energy-consumption management, which can also lead to reduced functionality, reliability, performance, and stability. The process of continuously collecting data is energy expensive for the devices. Following a period of battery discharge, the battery needs a period in which to recharge, but during that period, the device is unable to monitor continuously. When the charge of the battery is low, the device can experience a symptom comparable to wireless interference [ 11 ]. Where there is insufficient power for the wireless sensor nodes to operate, a severe issue is presented. To enable the nodes to function at low power, more effort should be dedicated to devising energy-efficient solutions, renewable technology, and green energy [ 8 ].

The IoT offers flexibility for monitoring patients who require ongoing medical evaluation, allowing the patient to live at home rather than being in the hospital. However, some patients find wearable devices to be uncomfortable. The data can become noisy as they are first transmitted from the sensor to the control device, and then, forwarded to the monitoring center. With superior architecture, data can be shared with minimal loss of integrity. Data signals can also be enhanced by applying noise-removal techniques. Most methods currently used to monitor ECGs require the signal to be analyzed in a supervised manner, which makes the process more expensive and can result in detection errors. To reduce costs and improve efficiency, machine learning can be used to analyze signals [ 98 ].

Another parameter is the cost of medical services, and treatment equipment is more important than ever. Researchers need to discuss and put more effort into minimizing the costs associated with IoT healthcare systems. The high cost of monitoring equipment in the IoT healthcare system is a serious issue. IoT has not yet made treatment services accessible to the average individual at a reasonable price. The cost of medical equipment is increasing [ 99 , 100 ].

To improve users’ perceptions and experiences of such expensive devices, it is incumbent upon device developers, manufacturers, assessors and testers to address these issues without compromising on cost or quality [ 11 ]. If the challenges outlined above can be resolved, the future IoT in the healthcare sector will be improved.

6.3. Computational Intelligence-Based

Computational-intelligence technologies are still in their infancy. Advanced intelligent computational services are needed for IoT-based healthcare-monitoring systems since computational intelligence is always a backbone of healthcare; it is associated with Internet-related data collection, computations, and evaluation because computing in IoT-based healthcare-monitoring systems is performed on edge devices to optimize data, networks, and traffic accordingly. However, we cannot ignore the fact that edge devices have limited resources and processing power, so we cannot ignore their limitations [ 11 , 101 ].

6.4. Integration-Based

Integration refers to the connection of current devices or tools with external technology to ensure the accuracy and consistency of data over the course of their lifetime for future expansion. The integrity of the data is still plagued by unresolved problems. IoT-based monitoring systems, when extended and fused with other external device that have various advantages, will improve quality of life. The development of integrated tools will have a significant positive impact on the communications, processing, and services provided by integrated information systems. This means that the IoT healthcare-monitoring systems needs to be extended using various technologies or related technologies such as the cloud, SDN, etc. [ 11 , 102 ].

6.5. Energy-Based

Monitoring-based healthcare-related IoT devices have a limited battery life. These gadgets still use energy even when they are in energy-saving mode and are not expressly required to read sensors. Some functions must be performed even when the device is in energy-saving mode, but it has a power limitation. Many pieces of medical equipment always need batteries, especially wearables and equipment for patients who need continuous condition monitoring [ 99 ]. An ideal system that integrates low-power communications with a power-efficient hardware architecture is needed to allow prolonged monitoring. Reduced power consumption is an exciting area of study for activity-aware energy models. The performance can be changed from low to high by utilizing context-aware episodic sampling [ 101 , 103 ].

6.6. Disease-Prediction-Based

The IoT helps to diagnose and treat conditions including chronic diseases, helps with geriatric care, and is used in fitness programs, by accelerating early disease detection [ 87 ]. The projected healthcare system’s future scope will advance the development of medical care that can foretell a patient’s ailment at an early stage. This disease-prediction system will shorten the time it might take to diagnose a condition and assist clinicians in providing treatment as early as possible. This will improve medical services, improve outcomes for the medical healthcare business, and lower medical costs (such as lab tests, X-rays, and some other needless medical tests). Hence, for patients’ benefit, there is also a need to develop a low-cost, independent system that tracks key indicators, transmits information to the cloud or NLP, and notifies the patient early via the appropriate APP [ 100 , 104 , 105 ].

7. Suggestions and Recommendations

Based on existing studies and their limitations, there is a need to enhance and integrate wearable healthcare devices to connect with other future technology trends, to solve the communication problems and drawbacks of previous studies. Researchers need to ensure that any proposed systems are user-friendly, adaptable, and secure if they want to retain satisfied customers. Disease management and healthcare can benefit from the new opportunities presented by integrating wearable sensors into healthcare systems. The IoT can provide a solution by connecting health-monitoring devices and sensors to the cloud for 24/7 monitoring. Health records are secured on the server and are available instantly.

In the future, a system could be created to diagnose patients’ conditions for chronic diseases and COVID-19; this could help doctors to make the right decision and optimize health conditions, which could improve the functionality of healthcare systems based on the IoT by combining different technological approaches.

Such integration approaches include artificial intelligence (AI), fog computing, Big Data and Nano-Things (IoNT), software-defined networks (SDNs), and the tactile Internet (TI). AI, when integrated with IoT-based healthcare-monitoring systems, can help to generate meaningful and accurate results from sensor data. The fog/edge paradigm can be used to bring computing power closer to where it is needed. Big Data computing can also be utilized in IoT healthcare-monitoring systems because Big Data can make it possible to manage extremely large amounts of data efficiently. In addition, the other most recent technologies of the future, such as the IoNT, software-defined networks (SDNs), and the tactile Internet (TI), have the potential to further enhance the functionality of IoT-based healthcare systems and expand their capabilities in the future.

8. Conclusions

There are endless ways in which the IoT can improve medical care. These include reduced cost, and increased efficiency, accuracy, and performance. The benefits of using the IoT have made it possible to automate healthcare systems in the best way. In this respect, this work aims to be an introductory guide for those who will work in this field in the future, providing them with a detailed reference document related to the IoT and healthcare-monitoring systems. In this work, recent research on IoT-based health-monitoring systems have been reviewed and analyzed in a systematic way. The paper provides in-depth information on their benefits and significance, and a literature review. We also discuss IoT wearable things in healthcare systems and provide a classification of health-monitoring sensors, including the challenges and open issues regarding security and privacy and Quality of Service (QoS). Suggestions for future work have also been included.

In the future, we plan to analyze and evaluate various types of disease-based classification and IoT-based healthcare-monitoring systems. We also plan, in our next phase, to stress the integration of various recent technology trends, such as SDN and AI, with IoT-based healthcare-monitoring systems.

Acknowledgments

The authors would like to thank University Malaysia Pahang for providing financial support and laboratory facilities under Product Development Grant Scheme (PDU) No. PDU203229 and RDU No. 210317 as well as to thank the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant21CTAP-C163815-01).

Funding Statement

This research was funded by University Malaysia Pahang under Product Development Grant Scheme No. PDU203229 and RDU No. 210317 as well as supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant21CTAP-C163815-01).

Author Contributions

The authors of this article have contributed to this research paper as follows: Writing and preparation, S.A.; Review and visualization, A.N., W.A.J., M.A.M.A., A.K.B., M.A.-M.K. and S.-H.K.; Editing and revision, S.A., A.N. and A.K.B. All authors have read and agreed to the published version of the manuscript.

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