DETECTION OF PARKINSON'S DISEASE FROM VOCAL FEATURES USING RANDOM SUBSPACE CLASSIFIER ENSEMBLE

被引：0

作者：

Eskidere, Omer ^{[1
]}

Karatutlu, Ali ^{[1
]}

Unal, Cevat ^{[1
]}

机构：

[1] Bursa Orhangazi Univ, Dept Elect & Elect Engn, Bursa, Turkey

来源：

2015 TWELVE INTERNATIONAL CONFERENCE ON ELECTRONICS COMPUTER AND COMPUTATION (ICECCO) | 2015年

关键词：

Detection of Parkinson's disease; Ensemble method; Random subspace; K-nearest neighbor algorithm; SPEECH IMPAIRMENT;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Parkinson's disease (PD) is a neurological disorder which is diagnosed through clinical examinations and observations rated on Unified Parkinson's disease Rating Scale (UPDRS). However, in the earlier stages of the disease, this approach might be inconclusive and result in misdiagnosis. Therefore, expert systems are needed to increase the detection accuracy of PD. In this paper, a random subspace based classifier ensemble with k-nearest neighbor (k-NN) as the base classifier was investigated for detection of PD. It was found that the random subspace k-NN classifier ensemble can outperform the single k-NN for a PD recognition problem.

引用

页码：112 / 115

页数：4

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