- Tsatsral Amarbayasgalan ORCID:orcid.org/0000-0001-8399-655X15,
- Jong Yun Lee ORCID:orcid.org/0000-0001-5526-946X15,
- Kwang Rok Kim ORCID:orcid.org/0000-0002-6114-960516 &
- …
- Keun Ho Ryu ORCID:orcid.org/0000-0003-0394-905417,18
Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 11721))
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Abstract
The World Health Organization (WHO) reported that coronary heart disease (CHD) is one of the top causes of global mortality, and it is also highly ranked in Korea. The wrong lifestyle such as alcohol, tobacco, and high fatty food is directly involved in the main risk factors for CHD. In the early stage, it is possible to prevent suffering from CHD by an appropriate drug and healthy lifestyle which lead to effective treatment. In this paper, we propose a deep autoencoder based neural networks (DAE-NNs) to predict CHD risk. First, a dataset is divided into two groups by their divergence using a deep autoencoder model. Then, deep neural network (NN) classifiers are trained on each group of dataset separately. As a result, the performance measurements including accuracy, F-measure and AUC score reached 83.53%, 84.36%, and 84.02%, respectively in the Korean population. These results show that our proposed DAE-NNs approach outperformed typical data mining based classifiers for CHD risk prediction.
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References
World Health Organization (WHO).https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 01 Mar 2019
American Heart Association.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408160/pdf/nihms852024.pdf. Accessed 01 Mar 2019
Park, H.W., Li, D., Piao, Y., Ryu, K.H.: A hybrid feature selection method to classification and its application in hypertension diagnosis. In: Bursa, M., Holzinger, A., Renda, M.E., Khuri, S. (eds.) ITBAM 2017. LNCS, vol. 10443, pp. 11–19. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-64265-9_2
Park, H.W., Batbaatar, E., Li, D., Ryu, K.H.: Risk factors rule mining in hypertension: Korean National Health and Nutrient Examinations Survey 2007–2014. In: 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–4. IEEE (2016)
National Heart, Lung, and Blood Institute.https://www.nhlbi.nih.gov/health-topics/coronary-heart-disease. Accessed 01 Mar 2019
Nucleus Medical Media.http://www.nucleushealth.com/. Accessed 01 Mar 2019
Hausmann, H., Topp, H., Siniawski, H., Holz, S., Hetzer, R.: Decision-making in end-stage coronary artery disease: revascularization or heart transplantation. Ann. Thorac. Surg.64(5), 1296–1302 (1997)
Diamond, G.A., Forrester, J.S.: Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N. Engl. J. Med.300(24), 1350–1358 (1979)
Kim, H., Ishag, M.I.M., Piao, M., Kwon, T., Ryu, K.H.: A data mining approach for cardiovascular disease diagnosis using heart rate variability and images of carotid arteries. Symmetry8(6), 47 (2016)
Kim, J., Lee, J., Lee, Y.: Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare Inform. Res.21(3), 167–174 (2015)
Kim, J.K., Kang, S.: Neural network-based coronary heart disease risk prediction using feature correlation analysis. J. Healthcare Eng. (2017)
Greenland, P., LaBree, L., Azen, S.P., Doherty, T.M., Detrano, R.C.: Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA291(2), 210–215 (2004)
Brindle, P., et al.: Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ327(7426), 1267 (2003)
Sacco, R.L., et al.: Improving global vascular risk prediction with behavioral and anthropometric factors: the multiethnic Northern Manhattan Cohort Study. J. Am. Coll. Cardiol.54(24), 2303–2311 (2009)
Abdullah, A.S., Rajalaxmi, R.: A data mining model for predicting the coronary heart disease using random forest classifier. In: International Conference in Recent Trends in Computational Methods, Communication and Controls, pp. 22–25 (2012). International Journal of Computer Applications
Srinivas, K., Rani, B.K., Govrdhan, A.: Applications of data mining techniques in healthcare and prediction of heart attacks. Int. J. Comput. Sci. Eng. (IJCSE)2(02), 250–255 (2010)
Nahar, J., Imam, T., Tickle, K.S., Chen, Y.P.P.: Computational intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Syst. Appl.40(1), 96–104 (2013)
Chaurasia, V., Pal, S.: Early prediction of heart diseases using data mining techniques. Carib. J. Sci. Technol.1, 208–217 (2013)
Das, R., Turkoglu, I., Sengur, A.: Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl.36(4), 7675–7680 (2009)
KNHANES.https://knhanes.cdc.go.kr/knhanes/eng/index.do. Accessed 01 Mar 2019
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Pearson Education, Boston (2006)
Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., Kannel, W.B.: Prediction of coronary heart disease using risk factor categories. Circulation97(18), 1837–1847 (1998)
Amarbayasgalan, T., Jargalsaikhan, B., Ryu, K.: Unsupervised novelty detection using deep autoencoders with density based clustering. Appl. Sci.8(9), 1468 (2018)
Ezawa, K.J., Norton, S.W.: Constructing Bayesian networks to predict uncollectible telecommunications accounts. IEEE Expert11(5), 45–51 (1996)
Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning, (ICML-03), pp. 616–623 (2003)
Gao, D., Madden, M., Chambers, D., Lyons, G.: Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study. In: Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2383–2388 (2005)
Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826), by NRF funded by the Ministry of Education (No. 2017R1D1A1A02018718), by Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2017 (Grants No. C0541451), and by the Private Intelligence Information Service Expansion (No. C0511-18-1001) funded by the NIPA (National IT Industry Promotion Agency).
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Authors and Affiliations
Database Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Korea
Tsatsral Amarbayasgalan & Jong Yun Lee
School of Law, Chungbuk National University, Cheongju, 28644, Korea
Kwang Rok Kim
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam
Keun Ho Ryu
Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Korea
Keun Ho Ryu
- Tsatsral Amarbayasgalan
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- Jong Yun Lee
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- Kwang Rok Kim
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- Keun Ho Ryu
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Correspondence toKeun Ho Ryu.
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Editors and Affiliations
Massachusetts Institute of Technology, Lexington, MA, USA
Vijay Gadepally
Intel Corporation, Portland, OR, USA
Timothy Mattson
Massachusetts Institute of Technology, Cambridge, MA, USA
Michael Stonebraker
Stony Brook University, Stony Brook, NY, USA
Fusheng Wang
University of Washington, Seattle, WA, USA
Gang Luo
Google, Mountain View, CA, USA
Yanhui Laing
Lucerne University of Applied Sciences, Rotkreuz, Switzerland
Alevtina Dubovitskaya
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Amarbayasgalan, T., Lee, J.Y., Kim, K.R., Ryu, K.H. (2019). Deep Autoencoder Based Neural Networks for Coronary Heart Disease Risk Prediction. In: Gadepally, V.,et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_17
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