Predictive and risk stratification models using machine learning algorithms such as Support Vector Machines (SVMs), have been used in cardiology and medicine to improve patient care and prognosis. In this work, we have used SVM based Recursive Feature Elimination (SVM-RFE) methods to select patient attributes/features relevant to the etio-pathogenesis of complications following a drug eluting stent (DES) procedure. With a high dimensional feature space (145 features, in our case), and comparatively few patients, there is a high risk of 'over-fitting'. Also, for the model to be clinically relevant, the number of patient features need to be reduced to a manageable number, to be used in patient care. SVM-RFE selects subsets of patient features that have maximal influence on the risk of a complication. In our results, when compared with our initial model with all the 145 features, we obtained better performance of the classifiers with 75 top ranked patient features, a 50% reduction in the original dimensionality of the data space. There was a universal improvement in performance of all SVMs with different kernels and parameters. This method of feature ranking helps to determine the most informative patient features. Use of these relevant features improves the prediction of complications following a DES procedure.

Original languageEnglish (US)
Title of host publicationComputers in Cardiology 2009, CinC 2009
Number of pages4
StatePublished - 2009
Event36th Annual Conference of Computers in Cardiology, CinC 2009 - Park City, UT, United States
Duration: Sep 13 2009Sep 16 2009

Publication series

NameComputers in Cardiology
ISSN (Print)0276-6574


Other36th Annual Conference of Computers in Cardiology, CinC 2009
Country/TerritoryUnited States
CityPark City, UT

ASJC Scopus subject areas

  • Computer Science Applications
  • Cardiology and Cardiovascular Medicine


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