Predicting coronary artery disease with medical profile and gene polymorphisms data

Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. We observe that the learned Bayesian Networks identify many important dependency relationships among genetic variables, which can be verified with domain knowledge. Conforming to current domain understanding, our results indicate that related diseases (e.g., diabetes and hypertension), age and smoking status are the most important factors for CAD prediction, while the genetic polymorphisms entail more complicated influences.