Analyzing dengue outbreak patterns using integrated machine learning approaches: A study in Bangladesh.

Abstract

Dengue fever remains a persistent global health threat, particularly in Southeast Asia, the Pacific, and the Americas. This study aims to improve early detection and prediction of dengue outbreaks by addressing the challenges of data scarcity and complex transmission factors through a hybrid machine learning approach. We developed a methodology that integrates clustering and classification techniques to identify and predict seasonal patterns of dengue risk. Using regional data from Bangladesh, clustering was performed to uncover latent patterns, with optimal clusters selected based on low inertia and high silhouette scores. The supervised machine learning models were then trained on labeled data to classify dengue risk levels using key meteorological and demographic characteristics. Clustering analysis revealed well-defined structures within the data, with a silhouette score of 0.774, indicating robust clustering quality. The classification models demonstrated exceptional performance, achieving more than 99% in accuracy, precision, recall, and F1 score metrics. These models effectively identified high-risk periods and regions with strong seasonal trends in dengue incidence. Overall, this study presents a data-driven framework for the early detection of dengue outbreaks, supporting proactive public health strategies, while also contributing to the identification of dengue patterns and serving as a tool for controlling infectious diseases.