The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Zahra Amiri, Arash Heidari (), Mehdi Darbandi, Yalda Yazdani, Nima Jafari Navimipour (), Mansour Esmaeilpour, Farshid Sheykhi and Mehmet Unal
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Zahra Amiri: Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5157944533, Iran
Arash Heidari: Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5157944533, Iran
Mehdi Darbandi: Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Via Mersin 10, Gazimagusa 99628, Turkey
Yalda Yazdani: Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665931, Iran
Nima Jafari Navimipour: Department of Computer Engineering, Kadir Has Universitesi, Istanbul 34085, Turkey
Mansour Esmaeilpour: Computer Engineering Department, Hamedan Branch, Islamic Azad University, Hamedan 6518115743, Iran
Farshid Sheykhi: Department of Biomedical Engineering, School of Medical Sciences, Asadabad 6541843189, Iran
Mehmet Unal: Department of Computer Engineering, Nisantasi University, Istanbul 34485, Turkey

Sustainability, 2023, vol. 15, issue 16, 1-41

Abstract: With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

Keywords: health; artificial intelligence; machine learning; internet of behavior; medicine; IoT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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