Abstract:
This work proposes a unique method for identifying dengue risk zones in Bangladesh using
machine learning algorithms and large weather data. Dengue fever, a mosquito-borne
illness that is common in tropical areas, has a complicated interplay with climate factors.
Using GNB, Random Forest Classifier, Decision Tree Classifier, and Voting Classifier,
Our Machine learning model uses Gradient Boosting Classifier and Logistic Regression to
uncover subtle patterns in rain, temperature, and oxygen. The study combines past weather
data with reported dengue cases, employing a number of machine learning methods to
determine connections between environmental variables and illness incidence. Our
algorithm delivers nuanced risk evaluations by applying a complex ensemble of classifiers,
classifying regions as "High Risk," "No Risk," "Low Risk," and "Moderate Risk." It allows
for focused public health interventions, more effective use of resources, and proactive
dengue epidemic control. The proposed machine learning-based prediction model not only
tackles the current threat of dengue in Bangladesh, but it also serves as a tough tool that
can be adapted to changing climatic dynamics. This study adds to the larger conversation
on the connection of data science and public health by providing an adjustable and dynamic
framework for minimising the effect of vector-borne illnesses in climate-vulnerable areas.
