Utilizing Clustering Techniques to Analyze Climate and Environmental Factors Impacting Dengue Incidence in Bangladesh

This study explores the link between climatic factors and dengue fever in Bangladesh (2008–2018) using machine learning clustering techniques: K-means, DBSCAN, and Affinity Propagation (AP). Rainfall shows a moderate positive correlation (0.37) with dengue cases, while humidity has a weaker correlation (0.28). AP outperformed other methods, identifying high-risk periods and regions, particularly in the monsoon season, with superior evaluation metrics: Silhouette Score (0.49), Calinski-Harabasz Index (149.74), and Davies-Bouldin Score (0.73). The findings offer actionable insights for resource allocation and targeted public health interventions during peak dengue transmission.