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Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions.

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Bibliographic Details
Title: Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions.
Authors: Salcedo, Dixon, Guerrero, Cesar, Saeed, Khalid, Mardini, Johan, Calderon-Benavides, Liliana, Henriquez, Carlos, Mendoza, Andres
Source: Electronics (2079-9292); Dec2022, Vol. 11 Issue 23, p4015, 24p
Subject Terms: SARS-CoV-2, COVID-19, MACHINE learning
Abstract: Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study's findings, which are directions for applying ML to address COVID-19 and its variants. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study's findings, which are directions for applying ML to address COVID-19 and its variants. [ABSTRACT FROM AUTHOR]
ISSN:20799292
DOI:10.3390/electronics11234015