<![CDATA[Adolescent mental health has become an increasingly critical issue due to the rising prevalence of emotional and behavioral disorders among young individuals. Social pressure, academic demands, and psychological changes often trigger stress, anxiety, and even depression, which affect learning activities and social interactions. This study aims to develop a web-based system to detect mental disorder risk in adolescents using a machine learning approach with the Support Vector Machine (SVM) algorithm. Three open datasets from the Kaggle platform—Big Five Personality Test Dataset, Symptom2Disease Dataset, and Mental Health in Tech Survey Dataset—were utilized to integrate personality traits, physical conditions, and mental health indicators. The data underwent preprocessing involving duplicate removal, missing value imputation, standardization, and categorical-to-numerical transformation before being split into 70% training and 30% testing sets. The system was developed using the Agile Scrum methodology in an iterative and adaptive manner based on user feedback. The experimental results show that the SVM model with an RBF kernel achieved 91.3% accuracy, 89.7% precision, and 91.9% F1-score. The resulting system, can classify mental disorder risk levels and provide prevention recommendations according to the assessment results. With an interactive interface, this system is expected to assist adolescents in recognizing their mental conditions early, increase awareness of psychological well-being, and serve as a technologybased educational tool for mental health prevention. ]]>
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