IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v9y2023i4idhcseit2390448.html

Detection and Evaluation of Chronic Kidney Disease Using Machine Learning Techniques

Author

Listed:
  • S. SenthilKumar
  • T. S. Baskaran

Abstract

Scientists are eager to improve and improve Analytical tools for clinical diagnosis. Machine learning technique one of the tools used in clinical analysis and diagnosis. This research considers the implementation of data mining Classification tools in renal patient data sets.It can also be used as a large storage deviceNumber of data. It also helps in understanding diseasesIt paves the way for predicting the disease and its future consequences Sickness. The proposed method reveals levels Renal failure patient and treatment and clinical outcome.

Suggested Citation

  • S. SenthilKumar & T. S. Baskaran, 2023. "Detection and Evaluation of Chronic Kidney Disease Using Machine Learning Techniques," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 9(4), pages 443-445, August.
  • Handle: RePEc:jbh:ijsrcs:v9:y2023:i4:id:hcseit2390448
    Note: Article URL: https://ijsrcseit.com/CSEIT2390448
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT2390448
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/paper/CSEIT2390448.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    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:jbh:ijsrcs:v9:y2023:i4:id:hcseit2390448. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.