Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.

Malaria remains a critical global health crisis, placing a disproportionate burden on children under five in Uganda. To transition from broad surveillance to targeted intervention, this study applies interpretable machine learning to identify key socioeconomic predictors of malaria using the 2018-2019 Uganda Malaria Indicator Survey. By employing Random Forests for feature selection and Decision Trees for classification, we addressed the inherent class imbalance using robust metrics such as the F2-score, Matthews Correlation Coefficient, and Precision-Recall Curve. Specifically, the Random Under-Sampling technique enabled the model to achieve a Recall of 77%, prioritizing the reliable detection of true positives over simple accuracy. The analysis highlights the hierarchical importance of determinants such as household size, mosquito net ownership, and maternal education. The study's defining contribution is the extraction of explicit "if-then" rules that visualize how these factors combine to create risk profiles, particularly revealing distinct disparities across regions such as Busoga and West Nile. These interpretable findings empower policymakers with actionable, evidence-based insights, moving beyond simple prediction to facilitate the design of structural and region-specific public health strategies.