Analysis of COVID-19 Based on Several Machine Learning Techniques

Abstract

COVID-19 sweeps the world with high infection and high death rates. It is essential for researchers to find an effective model to predict the trend of epidemic. With the good performances of several traditional models in predicting and analyzing previous epidemics, we compare several popular machine learning methods, including multi polynomial regression, logistic growth model and Long Short-Term Memory (LSTM) in epidemic prediction. We use least squares method for feature selection to determine the most relevant features and we also scale the data according to different experiment environments. We measure the accuracy using Mean Squared Error (MSE) and R2. We conclude that the LSTM model is the most effective model among all the competitors with the highest R2 (R2 = 0.97). We find that LSTM model is the most effective model among all the competitors. Our study gives a good example of feature and model selection for epidemic prediction and attempts to make a significant contribution to the government and hospital to supply the public resources and provide drugs to handle the incoming issues.