GK based fuzzy clustering for the diagnosis of cardiac arrhythmia

Mehdi, Ahmed M., Zayegh, Aladin, Begg, Rezaul and Ali, Rubbiya (2010) GK based fuzzy clustering for the diagnosis of cardiac arrhythmia. International Journal of Computational Intelligence and Applications, 9 2: 105-123. doi:10.1142/S146902681000280X


Author Mehdi, Ahmed M.
Zayegh, Aladin
Begg, Rezaul
Ali, Rubbiya
Title GK based fuzzy clustering for the diagnosis of cardiac arrhythmia
Journal name International Journal of Computational Intelligence and Applications   Check publisher's open access policy
ISSN 1469-0268
1757-5885
Publication date 2010-06
Sub-type Article (original research)
DOI 10.1142/S146902681000280X
Open Access Status
Volume 9
Issue 2
Start page 105
End page 123
Total pages 19
Place of publication Covent Garden, London, United Kingdom
Publisher Imperial College Press
Language eng
Subject 1712 Software
1706 Computer Science Applications
2614 Theoretical Computer Science
Formatted abstract
Cardiac arrhythmia is one of the major causes of human death, and most of the time it cannot be predicted well in advance at the right time. Computational intelligence algorithms can help in extracting the hidden patterns of biological datasets. This paper explores the use of advanced and intelligent computational algorithms for automated detection, classification and clustering of cardiac arrhythmia (CA). Application of Fuzzy C-Mean and Extended Fuzzy C-Mean method to the arrhythmia dataset (165 normal healthy and 138 with CA) demonstrated their good CA classification capabilities. Fuzzy C Mean algorithm was able to classify the two group of data set with an overall accuracy of 97.2% [sensitivity 96.4%, specificity 98.12% and area under the receiver operating curve (AUC-ROC = 0.963)]. The classification accuracy improved significantly when GK-based extended Fuzzy was employed, and an overall accuracy of 99.14% was achieved (sensitivity 97.11%, specificity 99.18% and AUC-ROC = 0.995). These accuracy results were respectively, 19.02%, 7%, 9.14% and 11.06% higher when compared to multi-input single layer perceptron (SLP), feed forward back propagation (FFBP), self organizing maps (SOM) and support vector machine (SVM). The performance measures of fuzzy techniques were found to be better if a Principal Component Analysis (PCA) technique was used to preprocess the arrhythmia datasets.
Keyword Cardiac arrhythmia
Computational intelligence
Principal component analysis
Perceptron
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 1 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 27 Nov 2013, 12:09:10 EST by System User on behalf of Institute for Molecular Bioscience