DocumentCode :
1834875
Title :
Pattern recognition methods for multi stage classification of parkinson´s disease utilizing voice features
Author :
Caesarendra, Wahyu ; Putri, Farika T. ; Ariyanto, Mochammad ; Setiawan, Joga D.
Author_Institution :
Mech. Eng. Dept., Diponegoro Univ., Semarang, Indonesia
fYear :
2015
fDate :
7-11 July 2015
Firstpage :
802
Lastpage :
807
Abstract :
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.
Keywords :
diseases; feature extraction; medical signal detection; principal component analysis; signal classification; speech recognition; ART-KNN; AdaBoost; LDA; PCA; PD detection; Parkinsons disease detection; SVM; UCI data repository; University of California-Irvine data repository; adaptive boosting; adaptive resonance theory-Kohonen neural network; features extraction; k-nearest neighbor; linear discriminant analysis; multistage classification; pattern recognition; principal component analysis; support vector machine; voice features; Accuracy; Feature extraction; Pattern recognition; Principal component analysis; Support vector machines; Testing; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Intelligent Mechatronics (AIM), 2015 IEEE International Conference on
Conference_Location :
Busan
Type :
conf
DOI :
10.1109/AIM.2015.7222636
Filename :
7222636
Link To Document :
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