Explainable Artificial Intelligence for Identifying Psychological Risk Profiles of Youth Suicidal Ideation: A SHAP-Based Machine Learning Analysis
Keywords:
Youth suicidal ideation, explainable artificial intelligence, machine learning, SHAP, psychological risk profilesAbstract
Objective: The objective of this study was to develop and interpret an explainable machine learning model capable of accurately identifying psychological risk profiles associated with suicidal ideation among youth.
Methods and Materials: This study employed a cross-sectional design involving adolescents and young adults recruited from educational settings. Participants completed standardized self-report measures assessing suicidal ideation, depressive and anxiety symptoms, emotional dysregulation, hopelessness, perceived family and peer support, academic stress, bullying exposure, and problematic digital use. Multiple supervised machine learning algorithms were trained to predict suicidal ideation, with model performance evaluated using area under the receiver operating characteristic curve, accuracy, precision, recall, and F1-score. The best-performing ensemble model was selected and interpreted using Shapley Additive Explanations to generate both global feature importance and individual-level explanatory profiles.
Findings: Ensemble-based machine learning models significantly outperformed traditional classifiers, achieving excellent discriminative performance in identifying suicidal ideation. Depressive symptoms, hopelessness, and emotional dysregulation emerged as the strongest positive predictors, while perceived family support demonstrated a robust protective effect. SHAP-based analyses revealed substantial heterogeneity in risk patterns, identifying multiple psychological profiles characterized by internalizing distress, affective instability, interpersonal disconnection, and contextual stress. These profiles explained individual predictions with high transparency, demonstrating that similar levels of suicidal ideation risk can arise from distinct configurations of psychological and social factors.
Conclusion: The findings indicate that explainable artificial intelligence can simultaneously achieve high predictive accuracy and meaningful psychological interpretability in youth suicide risk assessment. SHAP-based machine learning offers a powerful framework for identifying individualized risk profiles.
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