An Expert System for Early Detection of Mental Health Conditions Using Certainty Factor and DASS-42

Abstract

Mental health problems such as depression, anxiety, and stress continue to increase in many countries, while access to professional services is still limited. Many digital screening systems use fixed scoring methods and do not consider uncertainty in user responses. This study developed a web-based expert system by combining the Depression Anxiety Stress Scales (DASS-42) and the Certainty Factor (CF) method to represent uncertainty in overlapping emotional symptoms and provide more flexible screening results. The knowledge base was prepared through consultation with a licensed clinical psychologist and converted into 42 production rules based on the DASS-42 items. Each rule was assigned a confidence value according to expert judgment. The system uses forward chaining to combine active rules and calculate confidence scores for depression, anxiety, and stress at the same time. System evaluation was conducted using 50 community cases aged 18–35 years and compared with independent expert assessment. The overall accuracy reached 86% (43 of 50 cases). The accuracy for each category was 88.2% for depression, 82.3% for anxiety, and 87.5% for stress. Most classification errors occurred between anxiety and stress, which may be related to overlapping symptoms in the DASS-42 instrument. The findings indicate that the proposed system can support early mental health screening through interpretable confidence-based results. However, this study used a limited dataset and only one expert in knowledge development. The system is intended as a screening support tool and not as a replacement for clinical diagnosis.