Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach

Abstract

Dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Least Squares Support Vector Machines (LS-SVM) in predicting future dengue outbreak. Data sets used in the undertaken study includes data on dengue cases and rainfall level collected in five districts in Selangor. Data were preprocessed using the Decimal Point Normalization before being fed into the training model. Prediction results of unseen data show that the LS-SVM prediction model outperformed the Neural Network model in terms of prediction accuracy and computational time.