Data-driven support vector regression-genetic algorithm model for predicting the diphtheria distribution

Abstract

Indonesia is one of the countries with the largest number of diphtheria sufferers in the world. Diphtheria is a case of re-emerging disease, especially in Indonesia. Diphtheria can be prevented by immunization. Diphtheria immunization has drastically reduced mortality and susceptibility to diphtheria, but it is still a significant childhood health problem. This study predicted the number of diphtheria patients in several regions using support vector regression (SVR) combined with the genetic algorithm (GA) for parameter optimization. The area is grouped into 3 clusters based on the number of cases. The proposed method is proven to overcome overfitting and avoid local optima. Model robustness tests were carried out in several other regions in each cluster. Based on the experiments in three scenarios and 12 areas, the hybrid model shows good forecasting results with an average mean squared error (MSE) of 0.036 and a symmetric mean absolute percentage error (SMAPE) of 41.2% with a standard deviation of 0.075 and 0.442, respectively. Based on experiments in various scenarios, the SVR-GA model shows better performance than others. Compares two- means tests on MSE and SMAPE were given to prove that SVR-GA models have better performance. The results of this forecasting can be used as a basis for policy-making to minimize the spread of diphtheria cases.