An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach

Authors:
Hui-Ling Chen;Chang-Cheng Huang;Xin-Gang Yu;Xin Xu;Xin Sun;Gang Wang;Su-Jing Wang
Affiliations:
College of Physics and Electronic Information, Wenzhou University, Wenzhou, Zhejiang 325035, China;College of Physics and Electronic Information, Wenzhou University, Wenzhou, Zhejiang 325035, China;Guangzhou Military Region, Guang Zhou, Guang Dong 510000, China;China Classification Society, Digital Easy Technology Development Co. LTD., Beijing 100007, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest neighbor (FKNN) for Parkinson's disease (PD) diagnosis. The proposed FKNN-based system is compared with the support vector machines (SVM) based approaches. In order to further improve the diagnosis accuracy for detection of PD, the principle component analysis was employed to construct the most discriminative new feature sets on which the optimal FKNN model was constructed. The effectiveness of the proposed system has been rigorously estimated on a PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results have demonstrated that the FKNN-based system greatly outperforms SVM-based approaches and other methods in the literature. The best classification accuracy (96.07%) obtained by the FKNN-based system using a 10-fold cross validation method can ensure a reliable diagnostic model for detection of PD. Promisingly, the proposed system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.