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.
Keywords
big data, coronavirus, COVID-19, deep learning, information systems, internet of medical things, Internet of Things (IoT), machine learning
DOI Link
Publication Date
2022-06-23
Publication Title
Frontiers in Public Health
Volume
10
Deposit Date
2026-07-15
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Band, Shahab S.; Ardabili, Sina; Yarahmadi, Atefeh; Pahlevanzadeh, Bahareh; Kiani, Adiqa Kausar; Beheshti, Amin; Alinejad-Rokny, Hamid; Dehzangi, Iman; Chang, Arthur; Mosavi, Amir; and Moslehpour, Massoud, "A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis" (2022). Research Outputs: 2025-Present. 9.
https://arrow.tudublin.ie/itbinfo2ro/9