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Support Vector Machines Classification for Discriminating Coronary Heart Disease Patients from Non-coronary Heart Disease

Issue: 
Pages: 
451–7

ABSTRACT

Objective: The present contribution concentrates on the application of support vector machines (SVM) for coronary heart disease and non-coronary heart disease classification.

Methods: We conducted many experiments with support vector machine and different variables of low density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), total cholesterol (TC), triglycerides (TG), glucose and age (dataset 346 patients with completed diagnostic procedures). Linear and non-linear classifiers were compared: linear discriminant analysis (LDA) and SVM with a radial basis function (RBF) kernel as a non-linear technique.

Results: The prediction accuracy of training and test sets of SVM were 96.86% and 78.18% respectively, while the prediction accuracy of training and test sets of LDA were 90.57% and 72.73% respectively. The cross validated prediction accuracy of SVM and LDA were 92.67% and 85.4%.

Conclusion: Support vector machine can be used as a valid way for assisting diagnosis of coronary heart disease.

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e-Published: 03 Jul, 2013
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