Prediction of malaria incidence in Banggai Regency using Evolving Neural Network

Malaria is an endemic disease in most of area in Indonesia, especially in rural and remote areas. Banggai, one of regencies in Central Sulawesi province, is a high endemic area of malaria with Annual Parasite Incidence (API) in 2010 reached 7.88‰. The incidence and spreading of malaria were influenced by environmental and weather factors, particularly rainfall and temperature. Therefore this study would like to develop a malaria incidence prediction system based on environmental and weather factors, so that it may assist Indonesian Ministry of Health to control malaria. The method used to solve the problem was Evolving Neural Network (ENN). This method was a mixture between Artificial Neural Network (ANN) and Genetic Algorithm (GA). The result of this study shows that the prediction system has acceptable performance for predicting malaria incidence based on weather factors. The best performance in predicting malaria incidence in 2008 was 21.3% MAPE, 75% accuracy, and 84.21% F-value. While in predicting malaria incidence in 2009 was resulted 15.29% MAPE, 75% accuracy, and 40% F-value. These findings proved that there was a sufficient correlation between weather and malaria incidence. ENN also improved the performance of ANN up to 14.84% in MAPE, 25% in accuracy and 40% in F-value.