Fuzzy model for the classification of Parkinson’s disease based on voice signals

This study presents a Mamdani-type fuzzy logic model for classifying Parkinson’s disease (PD) based on voice signals. The model demonstrates improved performance compared to several existing methods, achieving 97.2% accuracy, 0.9696 sensitivity, 1.0 specificity, and an F-measure of 0.98. These metrics suggest that the proposed model offers higher classification precision than previous approaches. By leveraging fuzzy logic, the model enhances interpretability and addresses some uncertainties inherent in medical data. While the results are promising, further validation with more extensive and diverse datasets is necessary before the model can be integrated into clinical decision support systems for the early diagnosis of PD.