Web-Based Expert System for Diagnosing Adolescent Mental Health Disorders Using Support Vector Machine and Naïve Bayes
Keywords:
Expert System, machine learning, mental health diagnosis, support vector machine, naive bayes, adolescentsAbstract
Mental health disorders are serious issues that can affect anyone, including adolescents. The lack of public awareness, limited access to mental health services, and the absence of practical diagnostic systems are major obstacles in the early detection and treatment of mental disorders. This study aims to design and develop a web-based expert system capable of providing early diagnosis for 10 (ten) types of mental health disorders in adolescents. The system employs the Support Vector Machine (SVM) and Naïve Bayes algorithms to enhance classification accuracy based on symptoms input by users. The system’s knowledge base is derived from literature, mental health experts, and the PPDGJ III (Indonesian Guidelines for the Classification of Mental Disorders). Additionally, the system is equipped with an error-prevention feature to minimize human error during data input. The results indicate that the system can deliver efficient and accurate preliminary diagnoses and can be easily accessed by adolescents and the general public through a web platform. Therefore, this system is expected to serve as an alternative solution for early detection of mental health disorders and to raise public awareness of the importance of mental well-being.
References
World Health Organization. (2021). Adolescent mental health. World Health Organization.
American Psychiatric Association. (2022). DSM-5-TR: Diagnostic and statistical manual of mental disorders (5th ed., text rev.). APA Publishing.
Patel, V., Flisher, A. J., Hetrick, S., & McGorry, P. (2007). Mental health of young people: A global public-health challenge. The Lancet, 369(9569), 1302–1313. https://doi.org/10.1016/S0140-6736(07)60368-7
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Zhang, H. (2004). The optimality of Naïve Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, 562–567.
Durkin, J. (1994). Expert systems: Design and development. Prentice Hall.
Turban, E., Sharda, R., Delen, D., & Aronson, J. E. (2011). Decision support and business intelligence systems (9th ed.). Pearson Education.
