Hybridization of Genetic Algorithm and Neural Network on Predicting Dengue Outbreak
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In Malaysia, prediction of dengue outbreak becomes crucial because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there are still insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models, some of these models still have constraints in finding good parameter with high accuracy. The aim of this paper is to propose a more promising model for predicting dengue outbreak by using a hybrid model based on genetic algorithm for the determination of weight in neural network model. Several model architectures were designed and the parameters adjusted to achieve optimal prediction performance. A sample comprising dengue and rainfall data of Kuala Selangor in Selangor, Malaysia collected from State Health Department of Selangor and Malaysian Meteorological Department was used in a case study to evaluate the proposed model. The performance of the overall architecture was analyzed and the result showed that architecture III performed significantly better than other architectures and it is therefore proposed as a useful approach in the problem of time series prediction of dengue outbreak
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