Diagnosis of Heart Disease Based on Meta Heuristic Algorithms and Clustering Methods

Roostaee, S.; Ghaffary, H.R

doi:10.22061/jecei.2016.570

Document Type : Original Research Paper

Authors

Islamic Azad University, Ferdows Branch, ferdows, Iran.

https://doi.org/10.22061/jecei.2016.570

Abstract

Data analysis in cardiovascular diseases is difficult due to large massive of information. All of features are not impressive in the final results. So it is very important to identify more effective features. In this study, the method of feature selection with binary cuckoo optimization algorithm is implemented to reduce property. According to the results, the most appropriate classification for support vector machine is featured diagnoses heart disease. The main purpose of this article is feature reduction and providing a more precise diagnosis of the disease. The proposed method is evaluated using three measures: accuracy, sensitivity and specificity. For comparison, a data set of Machine Learning Repository database including information about 303 people with 14 features was used. In addition to the high accuracy of current methods, are expensive and time-consuming. The results indicate that the proposed method is superior on other algorithms in terms of Performance, accuracy and run time.

Keywords

20.1001.1.23223952.2016.4.2.1.4

References

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Journal of Electrical and Computer Engineering Innovations (JECEI)

Diagnosis of Heart Disease Based on Meta Heuristic Algorithms and Clustering Methods

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Volume 4, Issue 2 - Serial Number 8
July 2016
Pages 105-110

Diagnosis of Heart Disease Based on Meta Heuristic Algorithms and Clustering Methods

References

References

Send comment about this article

Volume 4, Issue 2 - Serial Number 8July 2016Pages 105-110

Volume 4, Issue 2 - Serial Number 8
July 2016
Pages 105-110