Enhanced Parkinson's disease prediction using LDEFS feature selection and Mamdani fuzzy neural network

Results confirm that the integration of exhaustive feature ranking with fuzzy neural modeling enhances PD prediction performance while minimizing the need for human intervention. The inclusion of exercise-related patterns and optimized feature weighting leads to improved robustness in classification. Therefore, the proposed system offers a reliable and scalable solution for early Parkinson's disease diagnosis and has strong potential for clinical deployment.