Prediction of Parkinson's Disease Severity Based on Feature Optimization

Abstract

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Most of the existing PD severity prediction methods are based on baseline features, and generally select features with high relevance for dimensionality reduction. To further improve the prediction performance, the LDFSF and GWT-RF-Att methods are proposed respectively from the perspective of feature selection and feature transformation. The LDFSF method utilizes dynamic feature selection strategy based on SOM clustering to select feature subsets with high correlation, low redundancy, and high complementarity from voice features. The GWT-RF-Att method uses graph wavelet transform to extract the more effective feature set based on the baseline features, and uses random forest improved by attention mechanism to improve the prediction performance of the model. The results on the Parkinson's telemonitoring dataset show that the performance of the two methods is better than that of the existing comparison methods, thus verifying their effectiveness.