Research Article Open Access

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Nor Azura Husin1, Norwati Mustapha1, Md. Nasir Sulaiman1, Razali Yaacob1, Hazlina Hamdan1 and Masnida Hussin1
  • 1 University Putra Malaysia, Malaysia
Journal of Computer Science
Volume 12 No. 6, 2016, 300-306

DOI: https://doi.org/10.3844/jcssp.2016.300.306

Submitted On: 24 June 2015 Published On: 2 July 2016

How to Cite: Husin, N. A., Mustapha, N., Sulaiman, M. N., Yaacob, R., Hamdan, H. & Hussin, M. (2016). Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction. Journal of Computer Science, 12(6), 300-306. https://doi.org/10.3844/jcssp.2016.300.306

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).

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Keywords

  • Hybrid GANN
  • Genetic
  • Neural Network
  • Predicting