Covid Future Prediction Using Machine Learning

Abstract

Abstract: The Machine Learning models have long been used in numerous software application domains which demanded the identification and prioritization of adverse factors for a threat. Several prediction methods of machine learning domain are being popularly used to handle forecasting or soothsaying problems.Machine learning (ML) based forecasting methods have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. This study substantially demonstrates the capability of Machine Learning models to forecast the number of forthcoming patients,death cases and also recovered cases of COVID-19 which is presently considered as a implicit trouble to humanity. In particular, four standard forecasting supervised machine learning models, such as linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) have been used in this study to forecast the dangerous factors of COVID-19 that we have previously discussed. Three types of predictions are made by each of the models, such as the number of recently infected cases, the number of death, and the number of recoveries in the coming 10 days. The results have been proved that the exponential smoothing (ES) performs exceptionally well among all the used models followed by LR and LASSO which performs better in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs inadequately in all the prediction scenarios given the available dataset that has been collected from various resources.