Design and Development of a Predictive AI Model for Early Detection of Mental Disorders in Adolescents
DOI:
https://doi.org/10.62872/bx45ph86Keywords:
mental disorder, adolescents, artificial intelligence, prediction, Random Forest, DASS-21Abstract
Adolescents are a vulnerable age group for mental disorders such as depression, anxiety, and stress. Early detection is crucial to enable timely and appropriate interventions. This study aims to design and develop a predictive artificial intelligence (AI) model capable of identifying potential mental health issues in adolescents. The research applies a quantitative experimental approach, collecting data through the locally validated DASS-21 questionnaire. The data were analyzed using Random Forest, Support Vector Machine, and Multilayer Perceptron algorithms, evaluated by accuracy, precision, recall, and F1-score metrics. The findings indicate that the Random Forest model achieved the highest accuracy at 87.4%. The system was designed with a user-friendly interface that delivers prediction results along with initial intervention recommendations. This study offers a significant contribution to preventive efforts in adolescent mental health through adaptive, accurate, and ethical AI-based technology.
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Copyright (c) 2025 Robinson Robinson, Ahmad Ari Gunawan Sepriansyah, Ahmad Zarkasih, Selvia Damayanti (Author)
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
