IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v19y2024i1p1-21.html
   My bibliography  Save this article

Diagnosis of Cardiovascular Diseases by Ensemble Optimization Deep Learning Techniques

Author

Listed:
  • David Opeoluwa Oyewola

    (Department of Mathematics and Statistics, Federal University, Kashere, Nigeria)

  • Emmanuel Gbenga Dada

    (Department of Computer Science, Faculty of Physical Sciences, University of Maiduguri, Nigeria)

  • Sanjay Misra

    (Department of Applied Data Science, Institute for Energy Technology, Halden, Norway)

Abstract

Cardiovascular disease (CVD) is a variety of diseases that affect the blood vessels and the heart. The authors propose a set of deep learning inspired by the approach used in CVD support centers for the early diagnosis of CVD using deep learning techniques. Data were collected from patients who received CVD screening. The authors propose a prediction model to diagnose whether people have CVD or not and to provide awareness or diagnosis on that. The performance of each algorithm is compared with that of long-, short-time memory, feedforward, and cascade forward neural networks, and Elman neural networks. The results show that the ensemble deep learning classification and prediction model achieved 98.45% accuracy. Using the proposed early diagnosis model for CVD can help simplify the diagnosis of CVD by medical professionals.

Suggested Citation

  • David Opeoluwa Oyewola & Emmanuel Gbenga Dada & Sanjay Misra, 2024. "Diagnosis of Cardiovascular Diseases by Ensemble Optimization Deep Learning Techniques," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 19(1), pages 1-21, January.
    • Handle: RePEc:igg:jhisi0:v:19:y:2024:i:1:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.334021
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:19:y:2024:i:1:p:1-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.