Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach

Abstract

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.