Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta

Abstract

Dengue hemorrhagic fever (DHF) is a common disease in tropical countries such as Indonesia that is often fatal. Early predictions of DHF case numbers help reduce the risk of community transmission and help related authorities develop prevention plans and strategies. Previous research shows that temperature, rainfall, and humidity indirectly affect DHF spread patterns. Therefore, this research uses and compares three machine learning models—restricted Boltzmann machine-backpropagation neural network (RBM-BPNN), artificial neural network-genetic algorithm (ANN-GA), and artificial neural network-particle swarm optimization (ANN-PSO)—to predict DHF case numbers in DKI Jakarta, the capital of Indonesia, which is in the DHF red zone. RBM and PSO are used to calculate optimal initial weight and bias before starting the prediction stage with ANN; meanwhile, GA updates weight and bias during the backward pass in ANN. The data includes temperature, rainfall, and humidity, plus previous DHF case data for five districts in DKI Jakarta from Jan. 6, 2009, to Sept. 25, 2017. We used Arima, Autocorrelation, and Pearson correlation for pre-processing data. The DHF case data fluctuates strongly and requires the moving averages method. The data consists of 70% training data and 30% testing data. The results show that each district requires different model architectures for the best predictions. `The best RMSE prediction of DHF cases with RBM-BPNN in Central Jakarta is 3,78%; the best RMSEs using ANN-GA in North and East Jakarta are 5,65% and 5,99%, respectively. The ANN-PSO model had the largest RMSE value in every district, with an average of 8,43%.