A novel prediction system in dengue fever using NARMAX model

Abstract

This paper describes the development of nonlinear autoregressive moving average with exogenous, input (NARMAX) models in diagnosing dengue infection. The developed system bases its prediction solely on the bioelectrical impedance parameters and physiological data. Three different NARMAX model order selection criteria namely FPE, AIC and Lipschitz have been evaluated and analyzed. This model is divided two approaches which are unregularized approach and regularized approach. The results show that using Lipschitz number with regularized approach yield better accuracy by 88.40% to diagnose the dengue infections disease. Furthermore, this analysis show that the NARMAX model yield better accuracy as compared to autoregressive moving average with exogenous input (ARMAX) model in diagnosis intelligent system based on the input variables namely gender, weight, vomiting, reactance and the day of the fever as recommended by the outcomes of statistical tests with 76.70% accuracy.