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Deep Autoencoder Based Neural Networks for Coronary Heart Disease Risk Prediction

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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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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).

Author information

Authors and Affiliations

  1. Database Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Korea

    Tsatsral Amarbayasgalan & Jong Yun Lee

  2. School of Law, Chungbuk National University, Cheongju, 28644, Korea

    Kwang Rok Kim

  3. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    Keun Ho Ryu

  4. Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Korea

    Keun Ho Ryu

Authors
  1. Tsatsral Amarbayasgalan
  2. Jong Yun Lee
  3. Kwang Rok Kim
  4. Keun Ho Ryu

Corresponding author

Correspondence toKeun Ho Ryu.

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  5. University of Washington, Seattle, WA, USA

    Gang Luo

  6. Google, Mountain View, CA, USA

    Yanhui Laing

  7. 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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