Effective Detection of Parkinson’s Disease at Different Stages using Measurements of Dysphonia

Abstract

This paper addressees the problem of multiclass of Parkinson's disease by the characteristic features of person's voice. So we computed 22 dysphonia measures from 375 voice samples of healthy and people suffering from Parkinson's disease. We used the particle swarm optimization (PSO) feature selection method, with random forest and the linear discriminant analysis (LDA) along with the 4-fold cross validation analysis to classify the subjects in 4 classes according to the severity of symptoms. With a classification accuracy score of 95.2%, promisingly the proposed diagnosis system might serve as a powerful tool for diagnosing PD, and could also extended for other voice pathologies.