An Optimized DBN-based Coronary Heart Disease Risk Prediction

  • Kahyun Lim Gachon University
  • Byung Mun Lee Gachon University
  • Ungu Kang Gachon University
  • Youngho Lee


Coronary Heart Disease (CHD) is the world’s leading cause of death according to a World Health Organization (WHO) report. Despite the evolution of modern medical technology, the mortality rate of CHD has increased. Nevertheless, patients often do not realize they have CHD until their condition is serious due to the complexity, high cost, and the side effects of the diagnosis process. Thus, research on predicting CHD risk has been conducted. The Framingham study is a widely-accepted study in this field. However, one of its limitations is its overestimation of risk, which threatens its accuracy. Therefore, this study suggests a more advanced CHD risk prediction algorithm based on Optimized-DBN (Deep Belief Network). Optimized- DBN is an algorithm to improve performance by overcoming the limitations of the existing DBN. DBN does not have the global optimum values for number of layers and nodes, which affects research results. We overcame this limitation by combining with a genetic algorithm. The result of genetic algorithm for deriving the number of layers and nodes of Optimized-DBN for CHD prediction was 2 layers, 5 and 7 nodes to each layers. The accuracy of the CHD prediction algorithm based on Optimized- DBN which is developed by applying results of genetic algorithm was 0.8924, which is better than Framingham’s 0.5015 and DBN’s 0.7507. In the case of specificity, Optimized-DBN based CHD prediction was 0.7440, which was slightly lower than 0.8208 of existing DBN, but better than Framingham’s 0.65. In the case of sensitivity, Optimized-DBN is 0.8549, which is better than Framingham 0.4429 and DBN 0.7468. AUC of suggesting algorithm was 0.762, which was much better than Framingham 0.547 and DBN 0.570.


[1] Blackwell, D. L.; Lucas, J. W. (2014); Summary health statistics for U.S. adults - national health interview survey, Vital Health statistics, 10(260), 1–161, 2014.

[2] Fielding, A. H. (1997); A review of methods for the assessment of prediction errors in conservation presence/absence models Environmental conservation, 24(1) 38–49, 1997.

[3] Freeman J. A.; Skapura, D. M. (1991); Algorithms, Applications, and Programming Techniques, Addison-Wesley Publishing Company, 1991.

[4] Hecht-Nielsen, R. (1992) Theory of the Backpropagation Neural Network Neural Networks for Perception, 65–93, 1992.

[5] Hinton, G. E.; Osindero, S.; Teh, Y.-W. (2006); A fast learning algorithm for deep belief nets Neural computation, 18(7), 1527–1554, 2006.

[6] Eom, J.-H.; Rhee, J.-K. (2006); AptaCDSS-A Cardiovascular Disease Level Prediction and Clinical Decision Support System using Aptamer Biochip, Korean Institute of Information Scientists and Engineers, 33, 28–32, 2006.

[7] Hinton, G.E.; Osindero S.; Teh, Y.W. (2006); A fast learning algorithm for deep belief nets, Neural computing, 18(7), 1527-1554, 2006.

[8] Holland J.H. (1984); Genetic Algorithms and Adaptation, In: Selfridge O.G., Rissland E.L., Arbib M.A. (eds), Adaptive Control of Ill-Defined Systems. NATO Conference Series (II Systems Science), Springer, Boston, MA, 16, 317-333, 1984.

[9] Ki, S.K.; Lee, S.M. (2014); Voice Activity Detection based on DBN using the Likelihood Ratio Journal of Rehabilitation Welfare Engineering& Assistive Technology, 8(3), 145–150, 2014.

[10] Korea Center for Disease Control and Prevention (2013); Guidelines for using raw data of Korean National Health and Nutrition Examination Survey - the first survey of the sixth phase (KNHANES VI-1), Ministry of Health and Welfare, 2013.

[11] Korea Encyclopedia Research Center (1996); Korea Encyclopedia Research Center: Nursary Encyclopedia, Korea Encyclopedia Research Center, 1996.

[12] Korea National Health and Nutrition Examination Survey (2013); [Online]. Available:

[13] Lewis, P.O. (1998); A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data Molecular Biology and Evolution, 15(3), 277–283, 1998.

[14] Mohamed, A.R.; Dahl, G.E.; Hinton, G. (2011); Acoustic modeling using deep belief networks IEEE Transactions on Audio, Speech, and Language Processing, 20(1), 14–22, 2011.

[15] National Institutes of Health (2001); NIH: National Cholesterol Education Program ATP III Guidelinesr, United States National Institutes of Health, 2001.

[16] Liu, N.; Jiang-ming Kan, J.-M. (2016); Improved Deep Belief Networks and Multi-Feature Fusion for Leaf Identification Neurocomputing, 216, 460–467, 2016.

[17] Park, R.W. (2017); Sharing Clinical Big Data While Protecting Confidentiality and Security: Observational Health Data Sciences and Informatics Healthcare Informatics Research, 23(1), 1–3, 2017.

[18] Townsend, J. T. (1974); Theoretical analysis of an alphabetic confusion matrix Perception & Psychophysic, 9(1), 40–50.

[19] Whitley, D. (1994); A genetic algorithm tutorial Statistics and computing, 4(2), 65–85, 1994.

[20] Wulsin, D.F.; Gupta, J. R.; Mani, R.; Blanco, J. A. (2011); Modeling electroencephalography waveforms with semi-supervised deep belief nets fast classification and anomaly measurement Journal of neural engineering, 8(3), 036015, 2011.

[21] Yan, X.; Chao, T.; Tu, K.; Zhang, Y. (2007); Improving the prediction of human microRNA target genes by using ensemble algorithm Federation of European Biochemical Societies, 581(8), 1586–1593, 2007.

[22] You, H.; Koo, M.-M.; Yi, K.; Nam, K. (2016); The Frequency based Study of the Applicability of DBN Algorithm on Language Acquisition Modeling The Korean Journal of Cognitive and Biological Psychology, 28 (4), 617–651, 2016.
How to Cite
LIM, Kahyun et al. An Optimized DBN-based Coronary Heart Disease Risk Prediction. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 4, p. 492-502, july 2018. ISSN 1841-9844. Available at: <>. Date accessed: 07 july 2020. doi:


Artificial Neural Networks (ANN), Deep Belief Network (DBN), Coronary Heart Disease (CHD), computational intelligence, genetic algorithm, CHD prediction