title: Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease.
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%.
  
  
  CONCLUSIONS
  Support vector machine can be used as a valid way for assisting diagnosis of coronary heart disease.
authors.affiliation: 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.
authors.forename: S
authors.initials: S
authors.lastname: Hongzong
authors.forename: W
authors.initials: W
authors.lastname: Tao
authors.forename: Y
authors.initials: Y
authors.lastname: Xiaojun
authors.forename: L
authors.initials: L
authors.lastname: Huanxiang
authors.forename: H
authors.initials: H
authors.lastname: Zhide
authors.forename: L
authors.initials: L
authors.lastname: Mancang
authors.forename: F
authors.initials: F
authors.lastname: BoTao
mesh_terms: 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