An analytic approach to better understanding and management of coronary surgeries

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.

► Demand for high-quality, affordable healthcare services increasing with the aging population in the US.
► Decision makers in healthcare need to be increasingly more effective and efficient at what they do.
► Analytic techniques should be used to augment accurate and timely decision making in healthcare.
► Machine learning techniques can be used to accurately predict CABG surgery outcome.
► Sensitivity analysis on trained models can be used to effectively explain prognostic factors.