Title :
A pattern recognition method for stage classification of Parkinson´s disease utilizing voice features
Author :
Caesarendra, Wahyu ; Ariyanto, Mochammad ; Setiawan, Joga D. ; Arozi, Moh ; Chang, Cindy R.
Author_Institution :
Mech. Eng. Dept., Diponegoro Univ., Semarang, Indonesia
Abstract :
This paper presents a pattern recognition method for multi-class classification of Parkinson´s disease based on PCA, LDA and SVM. 22 voice features which are extracted and reduced using PCA and LDA. SVM is then used during the classification step. The classification accuracy between single features and PCA and LDA features are presented and the results show that the PCA features have greater accuracy than LDA features and the single features.
Keywords :
diseases; feature extraction; medical computing; pattern classification; principal component analysis; support vector machines; Parkinson´s disease stage classification; feature classification accuracy; linear discriminant analysis; pattern recognition method; principal component analysis; support vector machines; utilizing voice features; Accuracy; Feature extraction; Pattern recognition; Principal component analysis; Support vector machines; Testing; Training;
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on
DOI :
10.1109/IECBES.2014.7047636
