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Comparing performance of data mining algorithms in prediction heart diseases

Abdar, Moloud, Kalhori, SRN, Sutikno, T, Subroto, IMI and Arji, G 2015, Comparing performance of data mining algorithms in prediction heart diseases, International journal of electrical and computer engineering, vol. 5, no. 6, pp. 1569-1576, doi: 10.11591/ijece.v5i6.pp1569-1576.

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Title Comparing performance of data mining algorithms in prediction heart diseases
Author(s) Abdar, MoloudORCID iD for Abdar, Moloud orcid.org/0000-0002-3059-6357
Kalhori, SRN
Sutikno, T
Subroto, IMI
Arji, G
Journal name International journal of electrical and computer engineering
Volume number 5
Issue number 6
Start page 1569
End page 1576
Total pages 8
Publisher Institute of Advanced Engineering and Science
Place of publication Yogyakarta, Indonesia
Publication date 2015-12
ISSN 2088-8708
2722-2578
Keyword(s) C5.0 algorithm
Data mining
Heart disease
Neural network
Summary Heart diseases are among the nation's leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases.After feature analysis, models by six algorithms including decision tree, neural network, support vector machine and k-nearest neighborhood developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.
Language eng
DOI 10.11591/ijece.v5i6.pp1569-1576
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Institute of Advanced Engineering and Science
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30134212

Document type: Journal Article
Collections: Open Access Collection
Deputy Vice-Chancellor Research Group
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.