DOI:
https://doi.org/10.64539/sjcs.v2i2.2026.480Keywords:
Mamdani Fuzzy Inference System, Respiratory Disease Classification, Exasens Dataset, Dielectric Permittivity, Fuzzy Logic, Clinical Decision SupportAbstract
Respiratory diseases remain a major global health concern, highlighting the need for accurate and interpretable computer-aided diagnostic systems. This study proposes a Mamdani Fuzzy Inference System (FIS) for the classification of four respiratory disease categories: Chronic Obstructive Pulmonary Disease (COPD), Asthma, Infected, and Healthy Control (HC). The proposed model utilizes the original variables provided in the Exasens dataset, including dielectric permittivity measurements (Real Permittivity Minimum, Real Permittivity Average, Imaginary Permittivity Minimum, and Imaginary Permittivity Average) together with demographic attributes (Age, Gender, and Smoking Status). A stratified subset of 100 records was selected from the publicly available Exasens dataset and preprocessed using min–max normalization before fuzzification with triangular and trapezoidal membership functions. Expert-defined fuzzy IF–THEN rules were employed within a Mamdani inference framework, and centroid defuzzification was used to obtain the final disease classification. The proposed model was evaluated using stratified 10-fold cross-validation and achieved an overall classification accuracy of 93.00%, with a macro-average F1-score of 91.87%. The experimental results demonstrate that the proposed Mamdani FIS provides accurate, transparent, and interpretable respiratory disease classification while preserving methodological reproducibility. These findings indicate its potential as a decision support tool for respiratory disease diagnosis.
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