title: Predicting complications of percutaneous coronary intervention using a novel support vector method.
abstract: OBJECTIVE
  To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).
  
  
  METHODS
  Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered.
  
  
  RESULTS
  The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases).
  
  
  CONCLUSIONS
  The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.
authors.affiliation: Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea. gyemin@seoultech.ac.kr
authors.forename: Gyemin
authors.initials: G
authors.lastname: Lee
authors.forename: Hitinder S
authors.initials: HS
authors.lastname: Gurm
authors.forename: Zeeshan
authors.initials: Z
authors.lastname: Syed
mesh_terms: D019540:Area Under Curve; D005240:Feasibility Studies; D006801:Humans; D016015:Logistic Models; D008955:Models, Cardiovascular; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods; D060388:Support Vector Machine
keywords: Computing Methodologies; Decision Support Systems; Percutaneous Coronary Intervention; Statistical Model; Support Vector Machines