Malaria Incidence Forecasting from Incidence Record and Weather Pattern using GMDH Polynomial Neural Network,
Published in International Conference on Future Computer and Communication (ICFCC 2013), 2013
Abstract - Malaria affects over 100 million persons worldwide with approximately 2,414 deaths a day in average each year. Indonesia is on the third highest position in the number of malaria incident in South East Asia, with 229,819 confirmed cases and 432 deaths only at 2010. Previous work has demonstrated the potential of neural networks in predicting the behavior of complex, non-linear systems. GMDH Polynomial Neural Network was applied in a great variety of areas for data mining and knowledge discovery, forecasting, systems modeling, optimization, and pattern recognition. Study has also shown the close relation between Malaria incidence and weather pattern. This paper proposed a modified GMDH Polynomial Neural Network to reduce the learning time and computation while maintaining the accuracy in predicting Malaria incidence by relating it to weather pattern. Based on the experiments, it was proven that the modified GMDH PNN was able to reduce the learning time by 72% and improve the accuracy into 88.02% compared to the original GMDH PNN.
Recommended citation: Anditya Arifianto, Ari Moesriami Barmawi, and Agung Toto Wibowo, “Malaria Incidence Forecasting from Incidence Record and Weather Pattern Using Polynomial Neural Network,” International Journal of Future Computer and Communication vol. 2, no. 6, pp. 60-65, 2014.