Applying Data-Mining Techniques to Analyze Affective Factors about the Enterovirus Epidemic

Abstract

In this paper, we explored factors that tend to increase the number of enterovirus infections. We use government open data and data-mining techniques such as linear regression, random forest, support vector machine, and gradient boosting implemented by the XGBoost package to predict the enterovirus epidemic in Taipei and Taoyuan next week. The R-squared (also known as the coefficient of determination) of the best performing predictive model is about 0.9, showing that we can effectively predict the enterovirus epidemic through machine learning models.