Advanced comparative analysis of machine learning algorithms for early Parkinson's disease detection using vocal biomarkers.

Objective: To investigate the potential of voice analysis-specifically sustained vowel phonation-as a non-invasive, cost-effective diagnostic method for early detection of Parkinson's disease (PD) using machine learning techniques. Methods: A publicly available dataset from the University of California, Irvine (UCI) repository, comprising 252 voice recordings (188 from PD patients and 64 from healthy individuals), was analyzed. Machine learning classifiers, including k-nearest neighbors (KNN), AdaBoost, and artificial neural networks (ANNs), were trained and tested on the dataset. Model evaluation was conducted using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. Kernel density estimation was applied to visualize and interpret classifier performance. Results: Among the classifiers, KNN demonstrated the best performance with an accuracy of 98.52% and a mean accuracy of 97.33%. ANN and AdaBoost achieved mean accuracies of 93.15% and 91.77%, respectively. All models performed well across standard evaluation metrics, indicating strong discriminative ability for detecting PD from voice data. Conclusion: The study confirms the feasibility of using sustained vowel phonation and machine learning for early PD diagnosis. The KNN classifier, in particular, shows excellent diagnostic accuracy. These findings support the integration of voice-based machine learning tools into clinical workflows, potentially enhancing early detection and management of PD.