IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_24.html
   My bibliography  Save this book chapter

A Novel Dyno-Quick Reduct Algorithm for Heart Disease Prediction Using Supervised Learning Algorithm

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • T. Marikani

    (Sree Muthukumaraswamy College, Department of Computer Science)

  • K. Shyamala

    (Dr. Ambedkar Govt. Arts College (Autonomous), P.G. and Research Department of Computer Science)

Abstract

Diagnosing of heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. Data classification can often be applied to medical data helps to detect the prevalence of disease. Many tools and algorithms are proposed by researchers to develop an effective medical decision system. Feature selection refers to the problem of Selecting relevant features to produce the most predictive outcome is called as feature selection. This paper proposes a new feature selection method based on rough set theory with modified dynamic quick reduct algorithm.

Suggested Citation

  • T. Marikani & K. Shyamala, 2020. "A Novel Dyno-Quick Reduct Algorithm for Heart Disease Prediction Using Supervised Learning Algorithm," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 267-274, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_24
    DOI: 10.1007/978-3-030-41862-5_24
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-030-41862-5_24. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.