Publications

Abstract : This work primarily aims to automatically detect patients who are suffering from Parkinson's disease (PD) in comparison to the individuals who are healthy, through voice samples under clean and different noisy environmental conditions. The data set was subjected to colored noise, electronic noise and natural noise. A feature vector comprising seven mean spectral features and two mean temporal features have been extracted. The performance of the PD detection model, configured by different classifiers of K-nearest neighbor (KNN), Extreme Gradient Boost, and Classification and Regression Trees (CART) have been analyzed under varying noisy environments. The proposed model for PD detection offers 97.01% accuracy for noise free dataset with KNN classifier and it also performs optimally even in the presence of varying noises.