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A Proposal of Expert System Using Deep Learning Neural Networks and Fuzzy Rules for Diagnosing Heart Disease

  • Hai Van PhamEmail author
  • Le Hoang Son
  • Le Minh Tuan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1013)

Abstract

A diagnosing heart disease is significant for researchers investigated to develop applications for improvement of an expert system in health care. The aim of this paper is to design an expert system in diagnoses using deep learning neural networks and fuzzy rules for physicians to make the right decisions in diagnosing heart disease. Based on the historical data of patients, our proposed approach is to quantify uncertain information represented in fuzzy rules in the Knowledge Base for diagnosing heart disease domain. The proposed approach helps doctors to make the right decision in the diagnosing heart disease. All doctors’ preferences can be updated to the Knowledge Base which can significantly improve the quality of heart disease diagnoses. The experimental results show that the proposed system is capable of diagnosing heart disease with a high confidence of accuracy in decision-making as compared to conventional neural networks.

Keywords

Expert systems Fuzzy rules Diagnosing heart disease Deep learning neural networks 

Notes

Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2017.02.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and Communication TechnologyHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Information Technology Institute, Vietnam National UniversityHanoiVietnam
  3. 3.Hanoi University of Home AffairsHanoiVietnam

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