Please use this identifier to cite or link to this item:
Title: Predicting coronary artery disease with medical profile and gene polymorphisms data
Authors: Chen, Q. 
Li, G. 
Leong, T.-Y. 
Heng, C.-K. 
Keywords: Bayesian networks
Coronary artery disease
data mining
machine learning
single nucleotide polymorphisms
Issue Date: 2007
Citation: Chen, Q.,Li, G.,Leong, T.-Y.,Heng, C.-K. (2007). Predicting coronary artery disease with medical profile and gene polymorphisms data. Studies in Health Technology and Informatics 129 : 1219-1224. ScholarBank@NUS Repository.
Abstract: 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. © 2007 The authors. All rights reserved.
Source Title: Studies in Health Technology and Informatics
ISBN: 9781586037741
ISSN: 09269630
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.