Dengue Disease Prediction by Machine Learning Algorithm in Bangladesh

Abstract

In Bangladesh, dengue fever is still a major public health issue that presents difficult management and preventative strategies. Proactive steps to lessen dengue's negative effects on public health can be made easier with accurate dengue epidemic prediction. In this work, we examine how well different machine learning methods predict the incidence of dengue fever in Bangladesh. We create a dataset with 1000 observations overall that consists of 9 input attributes and 1 outcome attribute. The input attributes comprise clinical markers such as NS1, IgG, and IgM levels in addition to demographic data like age and gender. Our prediction algorithm also incorporates geospatial variables like district, size, and kind of place. The output property "Outcome" indicates if dengue fever has occurred; this is a binary classification operation. The predictive performance of six machine learning algorithms is assessed, including Logistic Regression, Random Forest, Decision Trees, AdaBoost, Extreme Gradient Boosting (XGBoost), and LightGBM. With an astounding 98.67% accuracy rate, Random Forest is the most accurate of these algorithms. Our results highlight the potential of machine learning methods, especially Random Forest, to accurately forecast the incidence of dengue illness in Bangladesh. With the use of these prediction models, dengue outbreaks may be detected early and managed proactively, allowing for the prompt deployment of resources and the execution of focused intervention techniques to lessen the disease's crippling effect on public health systems.