SHAP-enhanced machine learning identifies modifiable obesity predictors across adolescent weight groups: A 2021 YRBSS analysis.

BACKGROUND: The growing prevalence of obesity in adolescents around the world poses a major threat to public health. This research uses machine learning models to examine the main causes of obesity, in contrast to standard information that typically rely on a single chance. The important fat-related steps were identified and ranked in this assessment to provide information on the effectiveness of the expected solutions. METHODS: Data from the 2021 Youth Risk Behavior Surveillance System (YRBSS) were used in a cross-sectional analysis of adolescents aged 12-18 years. Random Forest and XGBoost models were implemented to investigate behavioral, dietary, sleep, and substance use factors. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). RESULTS: Breakfast frequency, moderate-to-vigorous physical activity (MVPA) days, sleep duration, fruit intake, and screen time emerged as the most important predictors of obesity, with vaping also contributing to risk. Random Forest achieved an accuracy of 66.4% and XGBoost 66.3%, both with modest discriminative ability (AUC ~ 0.58). Fewer MVPA days, lower breakfast frequency, shorter sleep duration, lower fruit intake, and longer screen time were associated with increased obesity risk. SHAP analysis confirmed breakfast frequency and MVPA days as the top-ranked factors. CONCLUSION: Machine learning models identified key predictors of adolescent obesity, providing insights into the complex interplay of behavioral and lifestyle factors. Public health strategies should prioritize daily breakfast and fruit consumption, regular physical activity, sufficient sleep, reduced screen time, and vaping prevention to mitigate rising obesity rates among adolescents.