A survey on machine learning and internet of medical things-based approaches for handling COVID-19: meta-analysis

Shahab S. Band; Sina Ardabili; Atefeh Yarahmadi; Bahareh Pahlevanzadeh; Adiqa Kausar Kiani; Amin Beheshti; Hamid Alinejad-Rokny; Iman Dehzangi; Arthur Chang; Amir Mosavi; Massoud Moslehpour

doi:10.3389/fpubh.2022.869238

A survey on machine learning and internet of medical things-based approaches for handling COVID-19: meta-analysis

Shahab S. Band^*, Sina Ardabili, Atefeh Yarahmadi, Bahareh Pahlevanzadeh, Adiqa Kausar Kiani, Amin Beheshti, Hamid Alinejad-Rokny, Iman Dehzangi, Arthur Chang^*, Amir Mosavi^*, Massoud Moslehpour^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	869238
Pages (from-to)	1-22
Number of pages	22
Journal	Frontiers in Public Health
Volume	10
DOIs	https://doi.org/10.3389/fpubh.2022.869238
Publication status	Published - Jun 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

machine learning
covid-19
Internet of Things (IoT)
deep learning
big data
information systems
internet of medical things
coronavirus

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10.3389/fpubh.2022.869238Licence: CC BY

Publisher version (open access)Final published version, 541 KBLicence: CC BY

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Band, S. S., Ardabili, S., Yarahmadi, A., Pahlevanzadeh, B., Kiani, A. K., Beheshti, A., Alinejad-Rokny, H., Dehzangi, I., Chang, A., Mosavi, A., & Moslehpour, M. (2022). A survey on machine learning and internet of medical things-based approaches for handling COVID-19: meta-analysis. Frontiers in Public Health, 10, 1-22. Article 869238. https://doi.org/10.3389/fpubh.2022.869238

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A survey on machine learning and internet of medical things-based approaches for handling COVID-19: meta-analysis

Abstract

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Keywords

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A survey on machine learning and internet of medical things-based approaches for handling COVID-19: meta-analysis

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