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 care facility to predict the risk of complications at 12 months following a DES procedure. A significant challenge in this work, common to most clinical machine learning datasets, was imbalanced data, and our results showed the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) to address this issue. The developed predictive model provided an accuracy of 94% with a 0.97 AUC (Area under ROC curve), indicating high potential to be used as a decision support for management of patients following a DES procedure in real-world cardiac care facilities.