Predicting mental health risk in adolescents using interpretable machine learning: Evidence from SHAP analysis

Background/objective

Adolescent mental health is a critical public health concern; however, the combined contribution of emotional, demographic, and behavioral factors to poor mental health remains incompletely understood. This study applied an interpretable machine learning framework to identify key predictors of elevated mental health risk among adolescents and characterize their relative and non-linear contributions.