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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Prediction Of Coronary Artery Disease Using Core Principal Component Analysis Based Support Vector Machine

[Full Text]



Omprakash Subramaniam, Dr. Ravichandran Mylswamy



CAD, classification, feature selection, PCA, SVM



Data mining plays vital role in many fields. In medical field, the usage of data mining is getting increased day by day to predict the disease and classify its severity. Coronary Artery Disease (CAD) is becoming major reason for sudden death, which is getting increased in the South Asian countries. Hence, there exist a need to predict CAD by utilizing the patients history by using data mining algorithms. In order to solve this issue, this paper proposes a core framework for finding the indicators and fixing the thresholds to classify the patterns in the dataset; it utilizes the feature based mechanism which integrate principal-component-analysis (PCA) and support-vector-machine (SVM) for productive detection of patterns in the dataset. In dataset multiple features may be available, where few or more features might not be used in classification, even if used it may reduce the classification accuracy. The proposed classification algorithm wisely eliminates the features that are not required for performing the classification by introducing the new features. The results shows that the proposed algorithm outperforms the existing algorithms with 97.34%.



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