KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 6 Serial Number 40 2025 12 10 A Deep Neural Network Model for Predicting Stress Sensitivity in Adolescents Using Multidimensional Psychological Data A Deep Neural Network Model for Predicting Stress Sensitivity in Adolescents Using Multidimensional Psychological Data 1 11 10.61838/kman.jayps.4910 EN Journal Article 2025 07 29 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The objective of this study was to develop and evaluate a deep neural network model capable of accurately predicting stress sensitivity in adolescents based on a comprehensive set of psychological variables.</p> <p><strong>Methods and Materials: </strong>This cross-sectional study was conducted among secondary school adolescents in Malaysia using a school-based sampling design. Participants completed a battery of standardized self-report instruments assessing stress sensitivity, perceived stress, anxiety and depressive symptoms, emotion regulation strategies, psychological resilience, self-esteem, and social support, along with demographic information. After data preprocessing, including normalization and handling of missing values, a deep neural network with multiple hidden layers was trained to predict continuous stress sensitivity scores. The dataset was partitioned into training, validation, and test subsets, and model optimization was performed using adaptive gradient-based learning with regularization and early stopping to ensure generalizability. Model performance was evaluated using error-based and variance-based metrics, and comparative analyses were conducted against traditional statistical and machine learning models.</p> <p><strong>Findings: </strong>Inferential results indicated that the deep neural network achieved high predictive accuracy, explaining a substantial proportion of variance in stress sensitivity scores. The model significantly outperformed linear regression, support vector regression, and random forest models across all evaluation metrics. Permutation-based analyses revealed that perceived stress and anxiety symptoms were the strongest predictors, followed by emotion regulation through suppression, psychological resilience, depressive symptoms, and social support, while demographic variables contributed minimally. Cross-validation analyses demonstrated stable performance, supporting the robustness of the predictive framework.</p> <p><strong>Conclusion: </strong>The findings demonstrate that deep neural network models can effectively capture the complex, non-linear psychological processes underlying adolescent stress sensitivity and offer a promising data-driven approach for early identification of youth at heightened stress-related risk.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/4910/9310