Modeling Adolescent Mental Health, Suicide Risk, and Academic Performance Using YRBSS 2023 Data

Thomas M. Menino Convention & Exhibition Center 

Adolescent mental health, suicidal ideation, and academic performance are shaped by complex behavioral and environmental factors among U.S. teenagers. Using 2023 Youth Risk Behavior Survey data, we examine how feeling unsafe at school, unfair treatment, sexual assault, parent conflict, alcohol and drug use, and physical activity relate to suicidal thoughts, suicide attempts with injury, self-reported mental health, and grades. A central objective of this study is to improve the sensitivity of predictive models in correctly identifying adolescents who report suicidal ideation, experience mental health difficulties, or exhibit poor academic performance outcomes that are relatively rare yet critically important. We fit logistic regression, decision trees, random forests, and Naive Bayes classifiers to identify important predictors and potential interactions, using logistic models for interpretable associations and tree-based methods to highlight key decision pathways. Because suicidal outcomes are relatively rare, we apply resampling strategies, including SMOTE and distance based under sampling tailored to binary predictors, and compare their impact on sensitivity, specificity, and accuracy. Our findings demonstrate how the choice of sampling strategy meaningfully shapes model performance, with implications for the reliable identification of at‑risk adolescents in survey‑based research settings.

Adolescent mental health

Suicidal ideation and behavior

Supervised machine learning

Distance‑based under sampling

Predictive modeling 

Mental Health Statistics Section