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

Overview

Journal Front Public Health

Specialty Public Health

Date 2022 Jul 11

PMID 35812486

Authors

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

Affiliations

Soon will be listed here.

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.

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