Explainable AI Forecast of Psychological Distress in Adolescents Based on Family Conflict, School Pressure, and Emotion Regulation Capacity - Becker, Lars; Mehta, Rakesh (Journal of Adolescent and Youth Psychological Studies (JAYPS), 2025)
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model for forecasting psychological distress in adolescents by quantifying the joint and individual contributions of family conflict, school pressure, and emotion regulation capacity. Methods and Materials: This cross-sectional study was conducted among 1,142 secondary school students aged 13–18 years in Germany using multi-stage cluster sampling. Participants completed validated self-report measures of psychological distress, family conflict, school pressure, and emotion regulation capacity. Data were analyzed using an explainable gradient boosting machine learning framework with five-fold cross-validation. Model performance was evaluated using root mean square error, mean absolute error, and coefficient of determination. Feature contributions and interaction effects were examined using Shapley Additive Explanations and partial dependence analyses to ensure full interpretability of predictions. Findings: The explainable model demonstrated strong predictive accuracy, accounting for 69% of the variance in adolescent psychological distress on the test dataset (R² = 0.69, RMSE = 3.58, MAE = 2.71). Feature attribution analysis revealed that school pressure was the most influential predictor (36.2% relative contribution), followed by emotion regulation capacity (31.1%) and family conflict (24.7%), while demographic variables showed minimal impact. Interaction analyses indicated that high emotion regulation capacity substantially attenuated the negative effects of elevated school pressure and family conflict on psychological distress. Conclusion: Adolescent psychological distress is primarily shaped by the combined influence of academic stress, family dynamics, and emotional self-regulation. Explainable artificial intelligence provides a powerful and transparent framework for identifying individualized risk profiles and informing targeted mental health interventions in educational and clinical settings.