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.
Index Terms—Decimal Point Normalization, Dengue fever, Least Squares Support Vector Machines, Support Vector Machines.
Y. Yusof is with the College of Arts and Sciences, University Utara Malaysia. (phone: +604-928 4623; fax: +604-928 4753; e-mail: yuhanis@uum.edu.my).
Z. Mustaffa is with the College of Arts and Sciences, University Utara Malaysia. (e-mail: zuriani.m@gmail.com).
Cite: Yuhanis Yusof and Zuriani Mustaffa, "Dengue Outbreak Prediction: A Least Squares Support Vector Machines Approach," International Journal of Computer Theory and Engineering vol. 3, no. 4, pp. 489-493, 2011.
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