Feature Contribution Analysis of Youth Life Satisfaction Using Psychological Capital and Social Connectedness
Keywords:
Youth well-being, life satisfaction, psychological capital, social connectedness, explainable artificial intelligence, feature contribution analy, machine learning, adolescent mental healthAbstract
Objective: The objective of this study was to develop an explainable predictive model of youth life satisfaction by quantifying the individual and combined contributions of psychological capital components and social connectedness.
Methods and Materials: A cross-sectional correlational design was employed with a sample of 684 Moroccan adolescents and emerging adults recruited from secondary schools, vocational institutes, and universities. Participants completed standardized measures of life satisfaction, psychological capital, and social connectedness. Advanced machine learning models including Elastic Net Regression, Random Forest, and Gradient Boosting Machine were trained using ten-fold cross-validation. Explainable artificial intelligence techniques based on SHAP values and permutation feature importance were applied to interpret model predictions, identify dominant predictors, and examine nonlinear interactions among psychological and social variables.
Findings: The Gradient Boosting Machine demonstrated superior predictive performance (R² = .76, RMSE = 2.78, MAE = 2.19). Feature contribution analysis revealed that hope was the strongest predictor of life satisfaction, followed by social connectedness and self-efficacy. Optimism and resilience showed moderate but substantial contributions, whereas demographic variables such as age and gender exerted comparatively minor influence. Interaction effects indicated that high social connectedness amplified the positive effects of psychological capital components on predicted life satisfaction.
Conclusion: Youth life satisfaction is primarily driven by dynamic psychological and social resources rather than static demographic characteristics. Explainable machine learning provides a powerful framework for uncovering complex predictive structures and offers actionable insight for designing personalized youth well-being interventions focused on strengthening psychological capital and social integration.
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References
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