Model performance metrics.

BackgroundDiabetes remains a major public health concern in the United States, with a complex interplay of behavioral, demographic, and clinical risk factors. This study aims to identify the three best-performing machine learning models for diabetes risk prediction and to visualize the most influential predictors affecting diabetes likelihood. By leveraging a large, representative dataset, the study contributes to evidence-based strategies for targeted prevention.MethodsData were obtained from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), a nationally representative, population-based survey collecting information on health behaviors, chronic conditions, and preventive care. The analytical sample included 253,680 adult respondents and over twenty features encompassing sociodemographic variables (e.g., age, sex, race, income, education), health behaviors (e.g., smoking, physical activity, diet), and outcomes (e.g., BMI, hypertension, diabetes status). Eighteen machine learning models were trained and evaluated, including AdaBoost, Extra Trees Classifier, C5.0 Decision Tree, and CatBoost. Models were assessed using predictive accuracy and AUC scores. SHAP (SHapley Additive exPlanations) analysis was used to interpret the top model and examine how changes in key features influence diabetes risk.ResultsAmong the evaluated models, the Extra Trees Classifier achieved the highest predictive accuracy (>90%) and an AUC of 0.99. AdaBoost and CatBoost also demonstrated strong performance. Feature importance analysis identified BMI, age, general health status, income, physical health days, and education as the top predictors. A nonlinear association between income and diabetes risk was observed, with the highest prevalence in individuals earning $20,000–$25,000. Risk was also elevated in individuals aged 65–69 and those reporting poor general health. Hypertension showed a strong positive correlation with diabetes risk.ConclusionsMachine learning models, particularly tree-based ensemble methods, offer robust tools for diabetes risk prediction. These findings support their integration into public health analytics for personalized risk assessment and data-driven prevention strategies.