An analytic approach to better understanding and management of coronary surgeries

Delen D., Oztekin A., Tomak L.

DECISION SUPPORT SYSTEMS, cilt.52, sa.3, ss.698-705, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 3
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.dss.2011.11.004
  • Dergi Adı: DECISION SUPPORT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.698-705
  • Anahtar Kelimeler: Heart disease, Coronary artery bypass surgery (CABG), Clinical decision support systems, Survival prediction, Data mining, Machine learning, Sensitivity analysis, ARTIFICIAL NEURAL-NETWORK, BYPASS GRAFT-SURGERY, PREDICTION, MODELS, GUIDELINES, COST
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Demand for high-quality, affordable healthcare services increasing with the aging population in the US. In order to cope with this situation, decision makers in healthcare (managerial, administrative and/or clinical) need to be increasingly more effective and efficient at what they do. Along with expertise, information and knowledge are the other key sources for better decisions. Data mining techniques are becoming a popular tool for extracting information/knowledge hidden deep into large healthcare databases. In this study, using a large, feature-rich, nationwide inpatient databases along with four popular machine learning techniques, we developed predictive models; and using an information fusion based sensitivity analysis on these models, we explained the surgical outcome of a patient undergoing a coronary artery bypass grafting. In this study, support vector machines produced the best prediction results (87.74%) followed by decision trees and neural networks. Studies like this illustrate the fact that accurate prediction and better understanding of such complex medical interventions can potentially lead to more favorable outcomes and optimal use of limited healthcare resources. (C) 2011 Elsevier B.V. All rights reserved.