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An ensemble of -nearest neighbours algorithm for detection of Parkinson's disease

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  • Murat Gök

Abstract

Parkinson's disease is a disease of the central nervous system that leads to severe difficulties in motor functions. Developing computational tools for recognition of Parkinson's disease at the early stages is very desirable for alleviating the symptoms. In this paper, we developed a discriminative model based on a selected feature subset and applied several classifier algorithms in the context of disease detection. All classifier performances from the point of both stand-alone and rotation-forest ensemble approach were evaluated on a Parkinson's disease data-set according to a blind testing protocol. The new method compared to hitherto methods outperforms the state-of-the-art in terms of both predictions of accuracy (98.46%) and area under receiver operating characteristic curve (0.99) scores applying rotation-forest ensemble k-nearest neighbour classifier algorithm.

Suggested Citation

  • Murat Gök, 2015. "An ensemble of -nearest neighbours algorithm for detection of Parkinson's disease," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1108-1112, April.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:6:p:1108-1112
    DOI: 10.1080/00207721.2013.809613
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    References listed on IDEAS

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    1. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    2. Kemal Polat, 2012. "Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(4), pages 597-609.
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