IDEAS home Printed from https://ideas.repec.org/a/igg/jiit00/v20y2024i1p1-22.html
   My bibliography  Save this article

Intelligent Decision Support for Identifying Chronic Kidney Disease Stages: Machine Learning Algorithms

Author

Listed:
  • V. Shanmugarajeshwari

    (Kalasalingam Academy of Research and Education, India)

  • M. Ilayaraja

    (Kalasalingam Academy of Research and Education, India)

Abstract

The decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised in medical health data to identify chronic kidney disorders such as cancer and diabetes utilising computer-aided diagnosis. Deep learning is an intelligent area of machine learning in which neural networks are used to learn unsupervised from unstructured or unlabeled data. For CKD, the DL employed the deep stacked auto-encoder and soft-max classifier techniques. Kidney illness is another condition that can lead to a variety of health problems. Random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, DSAE, DNN, FNC, MLP are used in this study to predict and identify an early diagnosis of CKD patients using various machine and deep learning algorithms using R Studio and Python Colab software. The many stages of chronic kidney disease are identified in this paper.

Suggested Citation

  • V. Shanmugarajeshwari & M. Ilayaraja, 2024. "Intelligent Decision Support for Identifying Chronic Kidney Disease Stages: Machine Learning Algorithms," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 20(1), pages 1-22, January.
    • Handle: RePEc:igg:jiit00:v:20:y:2024:i:1:p:1-22
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.334557
    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:jiit00:v:20:y:2024:i:1:p:1-22. 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.