COVID-19 Future Forecasting Using Machine Learning

Abstract

predictions on behaviour after the surgery, which studies have shown, both with and without control, to be more
effective when using models and without a human subject bias (ML). In several applications where adverse risk variables
were observed and priority was given to machine learning models. Various methods of prediction are also used to deal with
prediction issues. This study shows the ability to estimate the number of COVID-9 patients who, via a machine learning
model, are seen as a possible threat to humanity. For COVID-19 threatening factors prediction four typical prediction
models were used: linear regression (LR), LORSO, vector aid (SVM), and exponential blending (ES). The number of new
patients affected, the number of deaths, and the number of recoveries are three ways to estimate for 10 days in each of the
models