Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering

Kemal Polat

International Journal of Systems Science, 2012, vol. 43, issue 4, 597-609

Abstract: This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California – Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstrated that the combination of the proposed weighting method called FCMFW and k-NN classifier has obtained very promising results on the classification of PD.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2011.581395 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:43:y:2012:i:4:p:597-609

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2011.581395

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().