IDEAS home Printed from https://ideas.repec.org/a/igg/jehmc0/v6y2015i2p1-9.html
   My bibliography  Save this article

ANN based Expert System to Predict Disease in Cardiac Patients at Initial Stages

Author

Listed:
  • Umer Rashid

    (Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan)

Abstract

Objective of this research is to develop an expert system for the preliminary investigation of cardiac abnormality in human beings. Artificial Neural Network (ANN) is judged best for the prediction of heart abnormalities in cardiac patients at initial stages. Our research is intended to employ an Artificial Intelligence (AI) technique in an automated solution, having minimum error bounds. An ANN based expert system is designed and developed, which identifies presence or absence of cardiac disease in patients by considering best practiced disease symptoms. The proposed expert system may help the clinicians in the preliminary investigation of cardiac abnormality in human beings.

Suggested Citation

  • Umer Rashid, 2015. "ANN based Expert System to Predict Disease in Cardiac Patients at Initial Stages," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 6(2), pages 1-9, April.
    • Handle: RePEc:igg:jehmc0:v:6:y:2015:i:2:p:1-9
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.2015040101
    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:jehmc0:v:6:y:2015:i:2:p:1-9. 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.