Review Of Machine Learning-Based Disease Diagnosis and Severity Estimation of Covid-19

Abstract

Following the identification of SARS-CoV-2, the novel coronavirus responsible for COVID-19, health care professionals have been pushed to develop novel technical solutions and patient treatment techniques. The COVID-19 outbreak has accelerated the deployment of machine learning (ML) technology, and various groups have been eager to embrace and adjust these ML solutions to the pandemic's concerns. We conducted a thorough assessment of the available literature on the use of machine learning in the fight against COVID-19, focusing on illness development, diagnosis, severity estimation, drug and treatment analysis, novel feature selection, and the post-Covid context. A systematic search of online research repositories such as Google Scholar, PubMed, and Web of Science was undertaken in accordance with the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" criteria to identify all relevant published papers between 2020 and 2022. The search syntax was created by combining COVID-19-specific terms with the word "machine learning."