A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

Journal: Frontiers in public health
Published Date: Jun 23, 2022

Abstract

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

Authors

  • Shahab S Band
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, Taiwan.
  • Sina Ardabili
    Department of Informatics, J. Selye University, Komárom, Slovakia.
  • Atefeh Yarahmadi
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
  • Bahareh Pahlevanzadeh
    Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran.
  • Adiqa Kausar Kiani
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
  • Amin Beheshti
    Department of Computing, Macquarie University, Sydney, NSW, Australia.
  • Hamid Alinejad-Rokny
    Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, 2052 Sydney, Australia; School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), 2052 Sydney, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia.
  • Iman Dehzangi
    Department of Computer Science, Rutgers University, Camden, NJ, United States.
  • Arthur Chang
    Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan.
  • Amir Mosavi
    Faculty of Informatics, Technische Universität Dresden, Dresden, Germany.
  • Massoud Moslehpour
    Department of Business Administration, College of Management, Asia University, Taichung, Taiwan.

Keywords

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