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.
