Coronary Artery Disease Prediction with Bayesian Networks and Constraint Elicitation
No Thumbnail Available
Files
Date
2006-09-12
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Coronary artery disease (CAD) is one of the major causes of death in the world. Finding cost-effective methods to predict CAD is a major challenge for public health. In this paper, we propose a Bayesian network learning approach with constraint elicitation mechanism to predict the risk of CAD. The underlying causal assumption and interpretability make Bayesian networks a good tool for medical applications, in this case CAD risk prediction involving both genetic and environmental factors. The constraint elicitation process improves model accuracy by incorporating relevant domain knowledge. We performed experiments to compare our results with those from other machine learning methods, such as naive Bayes, support vector machines, K nearest neighbors, neural networks and decision trees. Our method is shown to be comparable to these methods in terms of prediction accuracy but at the same time offers an intuitive representation of the relationships among variables in the problem domain. Conforming to the domain knowledge, the results identified the important environmental factors for CAD prediction and the relevant groups of gene markers contributing to the risk of CAD. The results also indicated that some gene markers that are relevant to CAD risk in western populations, but may not be relevant in Chinese, Indian and Malay populations local to Singapore.