Hybridization of Genetic Algorithm and Neural Network on Predicting Dengue Outbreak


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Abstract


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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Keywords


Hybrid; Neural Network; Regression; Prediction and Dengue Outbreak

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References


MOH (Ministry of Health Malaysia), Health Fact 2012, Health of Informatics Centre Planning and Development Division MOH/S/RAN/31.12(TR), Vol. 12, pp. 1-13, 2012.

Mahiran, M. and Ho, B.K., Clinical Practice Guideline on Management of Dengue infection in Adult, Journal of Malaysian family Physician 2011, Vol. 6, n. 2 & 3, 2011.

MOH (Ministry of Health Malaysia), Borang Peraturan- Peraturan Pencegahan Dan Pengawalan Penyakit Berjangkit (Borang Notis) 2005. Akta Pencegahan dan Pengawalan Penyakit Berjangkit 1988, 2005.

WHO (World Health Organization), Dengue: Guideline for Diagnosis, Treatment, Prevetion and Control, Geneva, 2009.

D.J. Gubler, “How Effectivelly is Epidemiological Surveillance used for Dengue Programme Planning and Epidemic Response?”, Dengue Bulletin, Vol. 26, 2002.

C.W. Lian, C.M. Seng and W.Y. Chai, Spatial, Environmental and Entomological Risk Factors Analysis on a Rural Dengue Outbreak in Lundu District in Sarawak, Malaysia, Tropical Biomedicine, Vol. 23(1), pp. 85-96, 2006.

S. B. Seng, A. K. Chong and, A. Moore. Geostatistical Modelling, Analysis and Mapping of Epidemiology of Dengue Fever in Johor State, Malaysia, The 17th Annual Colloquium of the Spatial Information Research Centre University of Otago, Dunedin, New Zealand, 2005.

N. Rachata, P. Charoenkwan, T. Yooyativong, , K., Chamnongthai C. Lursinsap and K. Higuchi, Automatic Prediction System of Dengue Haemorrhagic-Fever Outbreak Risk by Using Entropy and Artificial Neural Network. International Symposium on Communications and Information Technologies (ISCIT 2008), pp. 210-214, 2008.
http://dx.doi.org/10.1109/iscit.2008.4700184

WHO (World Health Organization), Dengue and Dengue Haemorrhagic Fevers, Dengue Bulletin, WHO Fact Sheet 117, http://www.who.int/inffs/en/fact117.html, 2002.

J.A. Patz, J.M.M. Willem, A.F. Dana and H.J. Theo. Dengue Fever Epidemic Potential as Projected by General Circulation Models of Global Climate Change, Environmental Health Perspective, vol. 106(3), pp.147-153, 1998.
http://dx.doi.org/10.1289/ehp.98106147

N.A. Husin N. Salim and A.R. Ahmad, “Modeling of Dengue Outbreak Prediction in Malaysia: A Comparison of Neural Network and Nonlinear Regression Model”, International Symposium on Information Technology (ITSIM 2008). pp. 1-4, 2008.
http://dx.doi.org/10.1109/itsim.2008.4632022

H.M. Aburas, B.G. Cetiner, and M. Sari, “Dengue Confirmed-Cases Prediction: A Neural Network Model”, Journal of Expert System with Application, vol. 37(2010), pp. 4256-4260, 2010.
http://dx.doi.org/10.1016/j.eswa.2011.04.120

J. R. Rabunal and J. Dorado, “Time Series Forecasting by Evolutionary Neural Network”, Artificial Neural Networks in Real-Life Applications, vol. 2:3, pp.58-59, 2006.
http://dx.doi.org/10.4018/978-1-59140-902-1

Nor Azura Husin, Norwati Mustapha, Md. Nasir Sulaiman and Razali Yaakob, A Hybrid Model using Genetic Algorithm and Neural Network for Predicting Dengue Outbreak, 2012 4th Conference on Data Mining andOptimization (DMO), 2012
http://dx.doi.org/10.1109/dmo.2012.6329793

E. S. Yuehjen, Developing a Hybrid Forecasting Model for Body Fat, (2010) International Review on Computer and Software (IRECOS), 5 (4), pp. 464-469.

G. Zhang, B.E. Patuwo, and M.Y. Hu, Forecasting with Artificial Neural Networks: The State of the Art, International Journal of Forecasting, pp. 35-62, 1998.
http://dx.doi.org/10.1016/s0169-2070(97)00044-7

P. Cabena, P. Hadjinian, R. Stadler, J. Verhees, and A. Zanasi, Discovering Data Mining From Concept to Implementation, Englewood Cliffs: Prentice Hall, 1998.

S.A. Hamid, and Z. Iqbal, Using Neural Networks For Forcasting Volatility of S&P 500 Index Future Price, Journal of Business Research,vol.75:pp.1116-1125,2004.

S. Rajasekaran and G.A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications (PHI Learning Private Limited, 2008)


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