KMAN Publication Inc. (KMANPUB) Journal of Psychosociological Research in Family and Culture 3041-8550 4 Serial Number 13 2026 01 01 Predicting Adolescent Risk Behavior via Family Monitoring, Sensation Seeking, and Peer Deviance with Machine Learning Analysis Predicting Adolescent Risk Behavior via Family Monitoring, Sensation Seeking, and Peer Deviance with Machine Learning Analysis 1 10 10.61838/kman.jprfc.5335 EN Journal Article 2025 07 07 <p><strong>Objective: </strong>The present study aimed to predict adolescent risk behavior by examining the combined effects of family monitoring, sensation seeking, and peer deviance using machine learning analytical approaches.</p> <p><strong>Methods and Materials: </strong>This cross-sectional predictive study was conducted among 512 adolescents aged 14 to 18 years recruited from secondary schools in Tunisia using multistage cluster random sampling. Data were collected using standardized self-report instruments, including the Youth Risk Behavior Surveillance Questionnaire, the Parental Monitoring Scale, the Brief Sensation Seeking Scale, and the Peer Delinquency Scale, all of which demonstrated acceptable reliability and validity. Statistical analyses were performed using SPSS-27 to compute descriptive indices and Pearson correlations. Machine learning models, including random forest, support vector machine, gradient boosting, and multilayer perceptron neural networks, were implemented in Python. Data preprocessing included normalization and feature scaling, and model performance was evaluated using k-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve.</p> <p><strong>Findings: </strong>The results revealed that adolescent risk behavior was significantly negatively associated with family monitoring and positively associated with sensation seeking and peer deviance. Among the predictive models, the neural network demonstrated the highest performance, achieving the greatest accuracy and discriminative power. Feature importance analyses consistently identified peer deviance as the strongest predictor of risk behavior, followed by sensation seeking, while family monitoring showed a weaker but still significant contribution. The models indicated that nonlinear interactions among predictors significantly improved prediction accuracy compared to linear approaches.</p> <p><strong>Conclusion: </strong>The findings highlight the critical role of peer deviance and sensation seeking in shaping adolescent risk behavior, while confirming the protective function of family monitoring. The superior performance of machine learning models underscores their utility in capturing complex behavioral patterns and enhancing predictive precision.</p> https://www.journals.kmanpub.com/index.php/jprfc/article/download/5335/9698