IDEAS home Printed from https://ideas.repec.org/p/ift/wpaper/1107.html
   My bibliography  Save this paper

An Empirical comparison of rule based classification techniques in medical databases

Author

Listed:
  • R.P.Datta

    (Indian Institute of Foreign Trade, Kolkata, India)

  • Sanjib Saha

    (Tata Consultancy Services, TCS New Building,Sector 5,Kolkata, India)

Abstract

Classification techniques have been widely applied in the field of medical databases and have gained a lot of success. At present various classification algorithms are available in the literature and the problem of choosing the best method for a particular data set is faced by many researchers. In this paper, we apply five well-known rule based classification techniques, Decision Tree, JRIP, NNGE, PART and RIDOR, on different medical databases and compare their relative merits & demerits. Subsequently, we interpret their applicability to segment patients into groups.

Suggested Citation

  • R.P.Datta & Sanjib Saha, 2011. "An Empirical comparison of rule based classification techniques in medical databases," Working Papers 1107, Indian Institute of Foreign Trade.
    • Handle: RePEc:ift:wpaper:1107
    as

    Download full text from publisher

    File URL: ftp://203.190.248.10/RePEc/ift/workingpapers/IT-11-07.pdf
    File Function: First version, 2011
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    classification; datamining; knowledge discovery; and rule based classification;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

    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:ift:wpaper:1107. 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: S. Balasubramanian (email available below). General contact details of provider: https://edirc.repec.org/data/iifttin.html .

    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.