A Methodical Investigation into the COVID-19 Risk Prediction Models

As a result of the COVID-19 pandemic, there is an urgent need for precise risk prediction models that can assist with the identification of individuals who are at a high risk of contracting the illness or experiencing severe outcomes. In this article, a comprehensive review of the previous research on COVID-19 risk prediction models has been done in the academic literature. Following the principles laid forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we have undertaken this analysis, and it features an exhaustive search of numerous databases The various models are identified, their quality is evaluated, and throughout the process, both their benefits and drawbacks are emphasized. These findings present recommendations for directions that future research should take and provide insights into the current state of COVID-19 risk prediction modeling. In this study, we also provide 10 recent research articles that evaluate a variety of machine learning models applied to healthcare, as well as COVID-19.