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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 k
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