6 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publication2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009
DOIs
StatePublished - Nov 23 2009
Event2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009 - Albuquerque, NM, United States
Duration: Aug 2 2009Aug 5 2009

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125

Other

Other2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009
CountryUnited States
CityAlbuquerque, NM
Period8/2/098/5/09

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ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

Cite this

Gouripeddi, R., Balasubramanian, V., Panchanathan, S., Harris, J., Bhaskaran, A., & Siegel, R. M. (2009). Predicting risk of complications following a drug eluting stent procedure: A SVM approach for imbalanced data. In 2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009 [5255454] (Proceedings - IEEE Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2009.5255454