Machine Learning Models for covid-19 future forecasting

Ramesh Kumar Mojjada; Arvind Yadav; A V Prabhu; Yuvaraj Natarajan

doi:10.1016/j.matpr.2020.10.962

Machine Learning Models for covid-19 future forecasting

Mater Today Proc. 2020 Dec 9. doi: 10.1016/j.matpr.2020.10.962. Online ahead of print.

Authors

Ramesh Kumar Mojjada¹, Arvind Yadav¹, A V Prabhu², Yuvaraj Natarajan³

Affiliations

¹ Department of Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India.
² Department of Electronics and communication engineering Koneru Lakshmaiah Education Foundation, Guntur.
³ ICT Academy, Chennai, Tamil Nadu, India.

Abstract

Computational methods for machine learning (ML) have shown their meaning for the projection of potential results for informed decisions. Machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. This study shows the ability to predict the number of individuals who are affected by the COVID-19[1] as a potential threat to human beings by ML modelling. In this analysis, the risk factors of COVID-19 were exponential smoothing (ES). The Lower Absolute Reductor and Selection Operator, (LASSo), Vector Assistance (SVM), four normal potential forecasts, such as Linear Regression (LR)). [2] Each of these machine-learning models has three distinct kinds of predictions: the number of newly infected COVID 19 people, mortality rates and the recovered COVID-19 estimates in the next 10 days. These approaches are better used in the latest COVID-19 situation, as shown by the findings of the analysis. The LR, that is effective in predicting new cases of corona, death numbers and recovery.

Keywords: COVID-19; R2 score adjusted; exponential process of smoothing; future forecasting; machine learning supervised.