Abstract
Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention of CHD denotes limiting a first CHD event in individuals who have not been formally diagnosed with the disease. This paper demonstrates how the integration of a Dynamic Bayesian network (DBN) and temporal abstractions (TAs) can be used for assessing the risk of a primary CHD event. More specifically, we introduce basic TAs into the DBN nodes and apply the extended model to a longitudinal CHD dataset for risk assesment. The obtained results demonstrate the effectiveness of our proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284 (2009)
Hulst, J.: Modeling physiological processes with Dynamic Bayesian networks. Master’s thesis, Man-machine interaction group Delft University of Technology, Delft, Netherlands (2006)
Jeni, L.A., Cohn, J.F., De La Torre, F.: Facing imbalanced data–recommendations for the use of performance metrics. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). pp. 245–251. IEEE Computer Society (2013)
Kurt, I., Ture, M., Kurum, A.T.: Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Systems with Applications 34(1), 366–3744 (2008)
Moon, T.K.: The Expectation-Maximization algorithm. IEEE Signal Processing Magazine 13(6), 47–60 (1996)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, Department of Computer Science, UC Berkeley (2002)
Orphanou, K., Stassopoulou, A., Keravnou, E.: Temporal abstraction and temporal Bayesian networks in clinical domains: A survey. Artificial Intelligence in Medicine 60(3), 133–149 (2014)
Rao, V.S.H., Kumar, M.N.: Novel approaches for predicting risk factors of atherosclerosis. IEEE Journal of Biomedical and Health Informatics 17(1), 183–189 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Orphanou, K., Stassopoulou, A., Keravnou, E. (2015). Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-19551-3_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
eBook Packages: Computer ScienceComputer Science (R0)