Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease.
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%.


CONCLUSIONS
Support vector machine can be used as a valid way for assisting diagnosis of coronary heart disease.
Hongzong
S
S
Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, Growing Base for State Key Laboratory, Qingdao University, Qingdao, Shandong, China.
Tao
W
W
Xiaojun
Y
Y
Huanxiang
L
L
Zhide
H
H
Mancang
L
L
BoTao
F
F
D000465:Algorithms; D001185:Artificial Intelligence; D003324:Coronary Artery Disease / Q000145:classification / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D003937:Diagnosis, Differential; D004867:Equipment Design; D006801:Humans; D010363:Pattern Recognition, Automated; D018570:Risk Assessment; D012984:Software