Ranking predictors of complications following a drug eluting stent procedure using Support Vector Machines

Ramkiran K Gouripeddi VN Balasubramanian Sethuraman Panchanathan J Harris A Bhaskaran RM Siegel
Abstract: 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, ...