PREDICTION OF COVID-19 FATALITY CASES BASED ON REGRESSION TECHNIQUES
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 3, Pages 696-719
Abstract
The COVID-19 was first reported to affect human life in Wuhan city, within the Hubei province of China in December 2019. An analysis of around 75,465 cases of COVID19 in China has revealed that the virus is transmitted between people from the blowout of respiratory droplets through sneezing and coughing. Corona is the world's most viral threat warning to people's health and the best pandemic in the world record. This paper presents a comparative study of regression techniques for the prediction of fatality cases due to Coronavirus. The objective of this paper is to predict the fatality cases of the top five affected countries which severely fight against COVID19. Time series forecasting analysis based on machine learning models like Linear Support Vector Regression (LSVR), Random Forest Regression, and Decision Tree Regressions are deployed to predict the fatality cases in the upcoming days. A comparative analysis is also carried out to identify which model best predicts the fatality cases. Covid-19 Data is considered from January 23, 2020, to October 30, 2020.
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