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

DOI10.1080/00207721.2011.581395zbMath1305.62375OpenAlexW2049474487MaRDI QIDQ5497351

Kemal Polat

Publication date: 4 February 2015

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2011.581395

zbMATH Keywords

Parkinson's disease\(k\)-NN classifierfuzzy \(c\)-means (FCM) clustering-based feature weighting

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Multivariate analysis and fuzziness (62H86)

Related Items (7)

Diagnosing Parkinson's diseases using fuzzy neural system ⋮ An intelligent Parkinson's disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach ⋮ New machine-learning algorithms for prediction of Parkinson's disease ⋮ An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach ⋮ A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection ⋮ An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease ⋮ Hesitant fuzzy agglomerative hierarchical clustering algorithms

Cites Work

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