Enhancing Cholera Outbreaks Prediction Performance in Hanoi, Vietnam Using Solar Terms and Resampling Data

Abstract

A solar term is an ancient Chinese concept to indicate a point of season change in lunisolar calendars. Solar terms are currently in use in China and nearby countries including Vietnam. In this paper we propose a new solution to increase performance of cholera outbreaks prediction in Hanoi, Vietnam. The new solution is a combination of solar terms, training data resampling and classification methods. Experimental results show that using solar terms in combination with ROSE resampling and random forests method delivers high area under the Receiver Operating Characteristic curve (AUC), balanced sensitivity and specificity. Without interaction effects the solar terms help increasing mean of AUC by 12.66%. The most important predictor in the solution is Sun’s ecliptical longitude corresponding to solar terms. Among the solar terms, frost descent and start of summer are the most important.