Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM)

In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is increasing rapidly in Malaysia, more work need to be done in order to prevent this situation from becoming critical. This includes work on predicting future dengue outbreak. This project proposes a prediction model incorporating Least Squares Support Vector Machines(LS-SVM) in forecasting future dengue outbreak. The data sets used in the undertaken study includes data on dengue cases data and rainfall for five districts in Selangor, from 2004-2005. Results obtained indicated that LS-SVM is capable of achieving better prediction accuracy and faster learning speed compared to Neural Network Model (NNM).