Mental Health Detection Expert System Model Based on DASS-42 Using Fuzzy Inference System - Eko Ginanjar Basuki Rahmat; Wiharto Wiharto; Umi Salamah (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
Mental health disorders such as depression, anxiety, and stress frequently co-occur and exhibit overlapping symptoms, making accurate diagnosis challenging due to the subjective nature of psychological assessments. Conventional use of the Depression Anxiety Stress Scales (DASS-42) relies on rigid score aggregation, while many machine learning approaches fail to adequately represent uncertainty and expert reasoning. This study aims to develop an expert system for mental health detection by integrating fuzzy logic with expert knowledge derived from the DASS-42 instrument. The main contribution of this research is a hybrid knowledge-based framework that combines decision tree–based rule extraction with psychological expert validation, ensuring both interpretability and clinical relevance. The proposed method employs a Fuzzy Inference System (FIS) using triangular and trapezoidal membership functions to model symptom intensity as linguistic variables, followed by rule generation using the CART decision tree algorithm and expert refinement. System performance is evaluated using Cohen’s Kappa coefficient, including standard error and 95% confidence intervals, to measure inter-rater reliability between the expert system, the DASS instrument, and two human experts. The results indicate that the expert system achieves almost perfect agreement in identifying dominant psychological conditions, with an average Kappa value of 0.918. For severity-level classification, strong agreement is observed for depression (Kappa = 0.842) and stress (Kappa = 0.811), while anxiety severity shows moderate-to-substantial agreement (Kappa = 0.648), reflecting inherent variability in expert interpretation. In conclusion, the proposed FIS-based expert system effectively captures expert diagnostic reasoning and outperforms decision tree–only models, demonstrating strong potential as an interpretable and reliable mental health screening tool.