Pattern recognition methods for multi stage classification of parkinson's disease utilizing voice features

A number of papers has presented a pattern recognition method for Parkinson's Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These features are extracted using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). It is found that PCA is better than LDA in terms of extracting significant features. Some classifiers such as Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN) and Adaptive Resonance Theory-Kohonen Neural Network (ART-KNN) are then used and compared. These methods are applied in multi stage classification. The classification results show that SVM has better testing accuracy than the other methods.