KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 6 Serial Number 40 2025 12 10 Psychological Resilience in Youth: A Machine Learning Analysis of Protective Factors and Stress Exposure Psychological Resilience in Youth: A Machine Learning Analysis of Protective Factors and Stress Exposure 1 11 10.61838/kman.jayps.4907 EN Journal Article 2025 07 28 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> This study aimed to identify and model the relative and interactive contributions of protective factors and cumulative stress exposure in predicting psychological resilience among youth using machine learning techniques.</p> <p><strong>Methods and Materials: </strong>A cross-sectional study was conducted with a diverse sample of adolescents and emerging adults from urban and peri-urban regions of South Africa. Participants completed validated self-report measures assessing psychological resilience, cumulative stress exposure, and a range of individual and contextual protective factors, including emotion regulation, self-efficacy, optimism, family support, peer support, and school belonging. Data were analyzed using supervised machine learning algorithms, including regularized linear models, support vector machines, random forest, and gradient boosting machines. Model performance was evaluated using repeated cross-validation procedures, and feature importance and interaction effects were examined through permutation-based methods and partial dependence analyses.</p> <p><strong>Findings: </strong>Inferential analyses indicated that ensemble machine learning models significantly outperformed linear approaches in predicting psychological resilience. The gradient boosting model explained a substantial proportion of variance in resilience scores and demonstrated high classification accuracy for distinguishing low, moderate, and high resilience profiles. Family support, emotion regulation, and self-efficacy emerged as the strongest predictors of resilience. Interaction analyses revealed that the protective effects of key resources intensified under higher levels of cumulative stress exposure, indicating robust stress-buffering effects.</p> <p><strong>Conclusion: </strong>The findings demonstrate that youth psychological resilience is best understood as a multidimensional and non-linear outcome shaped by dynamic interactions between stress exposure and protective factors. Machine learning approaches provide valuable tools for advancing resilience research by capturing complex patterns and identifying high-impact protective resources. These results underscore the importance of strengthening relational and emotional capacities to promote resilience among youth facing adversity.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/4907/9305