Optimized Support Vector Regression for Predicting Leishmaniasis Incidences

Abstract

Support Vector Regression (SVR) is a new approach in machine learning for time series prediction showing good performance. A big challenge for achieving optimal accuracy is the choice of appropriate parameters. In this paper, a Novel Enhanced Differential Evolution (NEDE) algorithm is proposed to calculate the optimal SVR parameters, and the combination approach (NEDE-SVR) was applied to predict the incidences of Zoonotic Cutaneous Leishmaniasis (ZCL) diseases. The NEDE-SVR based prediction model incorporates the climate factors as predictor variables, determined by analyzing their time lags related to the ZCL incidence. Conducted experiments have shown that NEDE-SVR exhibits good competitive performance using past diseases and climate data to predict the future cases of the ZCL disease. Accurate and timely ZCL disease predictions could aid structure health responses by informing key preparation and mitigation efforts.