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An Efficient Coronary Heart Disease Prediction by Semi parametric Extended Dynamic Bayesian Network with Optimized Cut PointsAn Efficient Coronary Heart Disease Prediction by Semi parametric Extended Dynamic Bayesian Network with Optimized Cut Points
K Gomathi, D Shanmuga Priyaa
Department of Computer Science Karpagam University, Karpagam Academy of Higher Education Coimbatore, Tamil Nadu, India 2Department of Information Technology Karpagam University, Karpagam Academy of Higher Education Coimbatore, Tamil Nadu, India
Abstract: Dynamic Bayesian Network (DBNs) is the general tool for enhancing the dependencies between the variables evolving in time and it’s used to represent the complex stochastic processes to study their properties or make predictions based on the future behavior. The coronary heart disease (CHD) is considered as the one of the -deadliest human diseases worldwide. The accurate prediction of CHD is very complex to be prevented and the treatment for it seems difficult. In early work, the TA methods with DBNs have been applied for the prognosis of the risk for coronary heart disease (CHD). The deviation of temporal abstractions from data is used for building DBN structure to predict CHD. However this approach cannot handle Complex temporal abstractions due to irregular time intervals. The cut-off values decided for temporal abstraction is the another issue in this work. In order to overcome this issue in this paper proposed the technique used for regularizing the irregular time interval in Extended Dynamic Bayesian Networks (DBNs) with temporal abstraction for coronary heart disease prediction. The proposed technique provides the global optimal solutions to assure the learning temporal solutions which provide observation of same irregularly spaced time points and the semi parametric subclass of the DBN proposed to allow further adaption of the irregular nature of the available data. The cut off value is searched from the domain expert knowledge base through the firefly optimization algorithm.
Keywords: Dynamic Bayesian Network, Firefly Optimization Algorithm, Temporal Abstraction An Efficient Coronary Heart Disease Prediction by Semi parametric Extended Dynamic Bayesian Network with Optimized Cut PointsAn Efficient Coronary Heart Disease Prediction by Semi parametric Extended Dynamic Bayesian Network with Optimized Cut Points
DOI:https://doi.org/10.6025/jes/2020/10/2/55-62
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References:

[1] Grzegorczyk, M., Husmeier, D. (2012). Bayesian regularization of nonhomogeneous dynamic Bayesian networks by globally coupling interaction parameters. In AISTATS (pp. 467-476).

[2] Robinson, J. W., Hartemink, A. J. (2010). Learning non-stationary dynamic Bayesian networks. Journal of Machine Learning Research, 11(Dec), 3647-3680.

[3] Orphanou, K., Stassopoulou, A., Keravnou, E. (2016). DBN-extended: a dynamic Bayesian network model extended with temporal abstractions for coronary heart disease prognosis. IEEE journal of biomedical and health informatics, 20(3), 944- 952.

[4] Rao, V. S. H., Kumar, M. N. (2013). Novel approaches for predicting risk factors of atherosclerosis. IEEE journal of biomedical and health informatics, 17(1), 183-189.

[5] Austin, R. M., Onisko, A., Druzdzel, M. J. (2010). The Pittsburgh cervical cancer screening model: a risk assessment tool. Archives of pathology & laboratory medicine, 134(5), 744-750.

[6] Exarchos, K. P., Exarchos, T. P., Bourantas, C. V., Papafaklis, M. I., Naka, K. K., Michalis, L. K., Fotiadis, D. I. (2013, July). Prediction of coronary atherosclerosis progression using dynamic Bayesian networks. In Engineering in Medicine and Biology Society (EMBC), 2013, 35th Annual International Conference of the IEEE (p 3889-3892). IEEE.

[7] MacLellan, W. R., Wang, Y., Lusis, A. J. (2012). Systems-based approaches to cardiovascular disease. Nature Reviews Cardiology, 9(3), 172-184.

[8] Arsene, O., Dumitrache, I., Mihu, I. (2011). Medicine expert system dynamic Bayesian Network and ontology based. Expert Systems with Applications, 38(12), 15253-15261.

[9] Gatti, E., Luciani, D., Stella, F. (2012). A continuous time Bayesian network model for cardiogenic heart failure. Flexible Services and Manufacturing Journal, 24(4), 496-515.

[10] Alai, M. S., Beig, J. R., Kumar, S., Yaqoob, I., Hafeez, I., Lone, A. A., Rather, H. A. (2016). Prevalence and characterization of coronary artery disease in patients with symptomatic bradyarrhythmias requiring pacemaker implantation. Indian heart journal, 68, S21-S25.

[11] Buchan, K., Filannino, M., Uzuner, O. (2017). Automatic prediction of coronary artery disease from clinical narratives. Journal of Biomedical Informatics.

[12] Marshall, A. H., Hill, L. A., Kee, F. (2010). Continuous Dynamic Bayesian networks for predicting survival of ischaemic heart disease patients. In Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on (p 178-183). IEEE.

[13] Goodwin, T., Harabagiu, S. M. (2015). A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records. AMIA Summits on Translational Science Proceedings, 2015, 61.


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