Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree.

Abstract

Dengue fever is a disease that has been outbreak worldwide in the last few years. Dengue is a fatal disease; sometimes, it may cause life-threatening complications and even death. Dengue is considered to be one of the critical diseases which is spreading in more than 110 countries. Nearly 45,000 case reports have been found around Bangladesh in the last year. Dengue fever has become a major health hazard in Bangladesh. Hence, early detection would mitigate major casualties of Dengue disease. Distinct studies have been performed concerning Dengue disease; however, no effective study, particularly from Bangladesh's perspective, it seemed that reveals Dengue outbreaks prediction method. In this scenario, this research work aims to analyse the Dengue disease and build an apposite model to predict dengue outbreaks. This paper also aims to find the best technique to early predicts Dengue disease. The real-time data of the patients admitted to different hospitals in Bangladesh is accumulated to achieve the goal of the current research. Then different multilayer perceptron neural networks and a Decision tree are used for Dengue outbreaks prediction. Twenty-five parameters are analysed to find these parameters' infection rates in this work. A comparative study of the developed models' performances is also accomplished to obtain a better Dengue outbreaks prediction model. The results evidence that the Levenberg-Marquardt is the best technique with 97.3% accuracy and 2.7% error in Dengue disease prediction. On the other hand, the Decision tree may have the second choice to assess Dengue disease.