© 2016 Nor Azura Husin, Norwati Mustapha, Md. Nasir Sulaiman, Razali Yaacob, Hazlina Hamdan and Masnida Hussin. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license. Journal of Computer Sciences Original Research Paper Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction Nor Azura Husin, Norwati Mustapha, Md. Nasir Sulaiman, Razali Yaacob, Hazlina Hamdan and Masnida Hussin Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Selangor, Malaysia Article history Received: 24-06-2015 Revised: 23-06-2016 Accepted: 01-07-2016 Corresponding Author: Nor Azura Husin Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Selangor, Malaysia Email: support@thescipub.com Abstract: Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN). Keywords: Hybrid GANN, Genetic, Neural Network, Predicting Introduction Dengue is a tropical mosquito-borne disease affecting more than 100 countries worldwide. Current estimate by WHO put the number of cases at 50-100 million cases per-year while the most recent estimate by a multinational study just published in the Lancet, tripled the WHO estimate at 360 million cases per year with 40% of the world population at risk (MOH, 2013). This disease may become the most important global health problem in the next decade which can no longer be ignored. This is further aggravated by environmental parameters like global warming, rapid urbanization and international traveling, which are recognized as contributing parameters to the spread of dengue outbreak (MOH, 2013). Dengue disease turns out to be the highest communicable disease compared to other prominent diseases like malaria, HFMD, typhus and yellow fever (MOH, 2012). In Malaysia, dengue cases were categorized as a notifiable disease in 1971. Since then, it continues to persist in predominantly urban and semi urban areas throughout the country. Approximately 70-80% of dengue cases are reported in areas where there is a high population density and rapid development activities which contribute to dengue transmission (Mahiran and Ho, 2011). Rapid urbanization has brought about enormous infrastructural build-up indirectly producing breeding areas for mosquito. Consequently, population growth and climate also considered as main parameters that contribute to the spike in dengue cases outbreak (Muhuiddin and Jamie, 2015). Previous research already shows that the accuracy of prediction model can be better over standalone model if we combine several different models. The hybrid models are proved in order to search the suitable parameter and make it model more robust with regard to the possible structure change in the data. Although combining or hybrid model prove to be alternative on solving the previous problem, there is not many existing prediction model using hybrid model especially prediction on dengue outbreak. Therefore, this study propose that the