Exploring Explainable Machine Learning for Predicting and Interpreting Self-Reported Diabetes among Tennessee Adults: Insights from the 2023 Behavioral Risk Factor Surveillance System (BRFSS).

backgroundDiabetes remains a major public health concern in the United States, particularly in Tennessee, where prevalence rates exceed national averages. Traditional statistical approaches may not fully capture the non-linear interactions among predictors. This study applied both traditional approaches and machine learning (ML) techniques to predict and identify key contributing factors associated with self-reported diabetes using the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset.

methodsA cross-sectional analysis was conducted on 5634 (weighted population 5 614 486) adults from the Tennessee BRFSS dataset. Sociodemographic, behavioral, and health-related variables were analyzed. Data processing, exploratory analysis, and modeling were performed in Python using Pandas, NumPy, Scikit-learn, and SHAP. Seven algorithms were tested: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, with stratified 5-fold cross-validation. Models were evaluated using accuracy, precision, recall, balanced accuracy, F1-score, AUROC, and PR-AUC.

resultsThe Gradient Boosting model demonstrated the best overall performance, achieving an accuracy of 82%, precision of 48%, recall of 32%, F1-score of 37%, AUROC of 0.80, and PR-AUC of 0.45. Key predictors included high blood pressure, high cholesterol, body mass index, comorbidity burden, and physical inactivity. SHAP analysis revealed that both clinical factors and social determinants substantially influenced diabetes risk.

conclusionThis study highlights the strong potential of machine learning, particularly Gradient Boosting, in predicting self-reported diabetes. Integrating SHAP analysis enhanced interpretability by revealing how the above factors interact to influence diabetes risk, underscoring the value of explainable AI for precision public health and targeted prevention strategies.