Predicting risk of complications following a drug eluting stent procedure: A SVM approach for imbalanced data

Ramkiran Gouripeddi Vineeth N Balasubramanian Sethuraman Panchanathan Jenni Harris Ambika Bhaskaran Robert M Siegel
Abstract: Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based on Support Vector Machines on a real world live dataset consisting of clinical variables of patients being treated at a cardiac ...