Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.

Dengue fever is a well-known vector-borne disease caused by Aedes aegypti mosquito. It has become a major burden to economy and society of affected country. In Malaysia, dengue incidence in Selangor has been worsening and alarming. The aim of this study is two-fold; to examine the trend and behaviour of dengue incidence across time and districts in Selangor and to cluster the endemic areas in Selangor using Wards hierarchical clustering method. The spatial and temporal analysis found that the dengue incidence is worsening in the early and middle of the year. The Wards minimum variance method was able to cluster Selangor's endemic area into high endemic areas (Gombak, Hulu Langat, Klang and Petaling), medium endemic area (Sepang) and low endemic areas (Hulu Selangor, Kuala Langat, Kuala Selangor, SabakBernam). The findings of the study are significant to respective local authorities in providing information for monitoring and planning the early dengue warning systems. This is important to reduce the dengue incidence in hot spot areas and to safeguard the community from dengue outbreak.

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