aceIdentifying risk factors for marijuana use among male high school students using machine learning: Implications for public health
Objectives To identify risk and protective factors associated with lifetime marijuana use among male high school students through an interpretable machine learning model, providing evidence to support early and targeted public health interventions. Study design Cross-sectional analysis of 2023 Youth Risk Behavior Surveillance System (YRBS) data for boys in grades 9-12 across the United States. Methods The final analytical sample included 8285 boys after excluding missing outcomes. Thirty-six predictors spanning…
An interpretable logistic regression model trained on YRBS data predicted lifetime marijuana use among male high school students with high accuracy, enabling early identification of at-risk boys for targeted school and community prevention.
Findings are based on a cross-sectional survey of only male high school students in the US with missing outcomes excluded, limiting causal inference and generalizability beyond this population.
Evidence
- Peer-reviewedPublic Health2026-08-22
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Truvace Impact Record TRV-2026-0864, v1: “Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health.” Truvace, 2026-08-24. /record/TRV-2026-0864 (accessed at citation time). sha256 81e844a457f98faa…
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