Predicting Adolescent Psychological Well-Being Using Gradient Boosting Models and Multidimensional Life Satisfaction Indicators
Keywords:
Adolescent well-being, life satisfaction, gradient boosting, machine learning, psychological health, explainable artificial intelligenceAbstract
Objective: The objective of this study was to examine the extent to which adolescent psychological well-being can be accurately predicted using gradient boosting machine learning models integrating multidimensional life satisfaction indicators.
Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Taiwan using a school-based sampling framework. Psychological well-being was assessed as a continuous outcome variable, while multidimensional life satisfaction domains—including emotional health, family life, peer relationships, school experience, academic self-satisfaction, physical health, neighborhood context, and perceived economic status—were used as predictive features alongside key demographic and behavioral covariates. Advanced gradient boosting algorithms were trained and validated using a hold-out testing approach with cross-validated hyperparameter optimization. Model performance was evaluated using inferential predictive metrics, and explainable machine learning techniques were applied to quantify feature contributions and non-linear effects.
Findings: Inferential results demonstrated that gradient boosting models explained a substantial proportion of variance in adolescent psychological well-being, with ensemble models achieving high predictive accuracy and low estimation error. Emotional health satisfaction emerged as the strongest predictor, followed by family life satisfaction and school life satisfaction, indicating statistically meaningful and non-linear contributions to well-being. Peer life satisfaction and academic self-satisfaction showed moderate but significant predictive influence, while health-related behaviors such as sleep duration exhibited curvilinear effects. Explainability analyses revealed marked inter-individual heterogeneity in predictor importance, supporting the presence of multiple predictive pathways to psychological well-being rather than a single dominant profile.
Conclusion: The findings indicate that adolescent psychological well-being can be robustly predicted using gradient boosting models that integrate multidimensional life satisfaction indicators.
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References
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