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Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

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Abstract

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique.

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Acknowledgements

This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.

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Authors and Affiliations

  1. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Mehrbakhsh Nilashi

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

    Mehrbakhsh Nilashi

  3. Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq

    Hossein Ahmadi

  4. Department of Cybersecurity, College of Computer Science and Engineering, University Of Jeddah, Jeddah, Saudi Arabia

    Azizah Abdul Manaf

  5. Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Kurdistan, Iraq

    Tarik A. Rashid

  6. Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Sarminah Samad

  7. Halal Research Center of IRI, FDA, Tehran, Iran

    Leila Shahmoradi

  8. Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

    Leila Shahmoradi

  9. Department of Information System and Technology, College of Computer Science and Engineering, University Of Jeddah, Jeddah, 23218, Saudi Arabia

    Nahla Aljojo

  10. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Elnaz Akbari

  11. Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Elnaz Akbari

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  1. Mehrbakhsh Nilashi
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  2. Hossein Ahmadi
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Correspondence to Mehrbakhsh Nilashi or Elnaz Akbari.

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Nilashi, M., Ahmadi, H., Manaf, A.A. et al. Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. Int. J. Fuzzy Syst. 22, 1376–1388 (2020). https://doi.org/10.1007/s40815-020-00828-7

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