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Deep Autoencoder Based Neural Networks for Coronary Heart Disease Risk Prediction

  • Tsatsral Amarbayasgalan
  • Jong Yun Lee
  • Kwang Rok Kim
  • Keun Ho RyuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11721)

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.

Keywords

Coronary heart disease Data mining Deep autoencoder Reconstruction error Neural network 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Database Laboratory, School of Electrical and Computer EngineeringChungbuk National UniversityCheongjuKorea
  2. 2.School of LawChungbuk National UniversityCheongjuKorea
  3. 3.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  4. 4.Department of Computer Science, College of Electrical and Computer EngineeringChungbuk National UniversityCheongjuKorea

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