Enhancing Parkinson’s Diagnosis with PhonatoryVoice Analysis: Optimizing Machine LearningFeature Selection for Support Vector Machines

Parkinson’s disease (PD) is a common neurological disorder that impairs mobility and significantly impacts patients’ quality of life. Early diagnosis of PD is crucial for timely intervention and management. This study explores the potential of computer-aided analysis, specifically machine learning, for early PD diagnosis, focusing on phonatory voice analysis using dysphonia measures from sustained vowels. To enhance PD diagnosis performances, this study investigates the effectiveness of two feature selection methods, L1-norm support vector machine (L1-norm SVM) and Recursive Feature Elimination (RFE), within the framework of Support Vector Machine (SVM) and data from UCI Machine Learning Repository and Chulalongkorn Centre of Excellence for Parkinson’s Disease \& Related Disorders, a leading medical center for treating PD patients in Thailand, were utilized. Furthermore, this research uses PD voice samples to analyze dysphonia features for diagnosing PD. The results suggest that both L1-norm SVM and RFE enhance the accuracy of PD diagnosis, with L1-norm SVM showing higher performance, achieving an accuracy of 95.97%, a positive recall score of 97.16%, and an F1-macro score of 95.69%. The study also highlights the importance of key dysphonia measures, including RPDE, DFA, and HNR, in differentiating PD voices from non-PD voices. Further research is needed to explore additional feature selection methods, audio features, and datasets, which would help uncover more characteristics in early PD voice analysis and further enhance the performance of PD diagnosis.