abdar-comparingperformance-2015.pdf (396.3 kB)
Comparing performance of data mining algorithms in prediction heart diseases
journal contribution
posted on 2015-12-01, 00:00 authored by Moloud Abdar, S R N Kalhori, T Sutikno, I M I Subroto, G ArjiHeart 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.
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Journal
International journal of electrical and computer engineeringVolume
5Issue
6Pagination
1569 - 1576Publisher
Institute of Advanced Engineering and ScienceLocation
Yogyakarta, IndonesiaPublisher DOI
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ISSN
2088-8708eISSN
2722-2578Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2015, Institute of Advanced Engineering and ScienceUsage metrics
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