IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v9y2014i4p1-12.html
   My bibliography  Save this article

Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers

Author

Listed:
  • Abeer Y. Al-Hyari

    (The University of Jordan, Amman, Jordan)

  • Ahmad M. Al-Taee

    (The University of Jordan, Amman, Jordan)

  • Majid A. Al-Taee

    (The University of Liverpool, Liverpool, UK)

Abstract

This paper presents a new clinical decision support system for diagnosing patients with Chronic Renal Failure (CRF) which is not yet thoroughly explored in literature. This paper aims at improving performance of a previously reported CRF diagnosis system which was based on Artificial Neural Network (ANN), Decision Tree (DT) and Naïve Bayes (NB) classifying algorithms. This is achieved by utilizing more efficient data mining classifiers, Support Vector Machine (SVM) and Logistic Regression (LR), in order to: (i) diagnose patients with CRF and (ii) determine the rate at which the disease is progressing. A clinical dataset of more than 100 instances is used in this study. Performance of the developed decision support system is assessed in terms of diagnostic accuracy, sensitivity, specificity and decisions made by consultant specialist physicians. The open source Waikato Environment for Knowledge Analysis library is used in this study to build and evaluate performance of the developed data mining classifiers. The obtained results showed SVM to be the most accurate (93.14%) when compared to LR as well as other classifiers reported in the previous study. A complete system prototype has been developed and tested successfully with the aid of NHS collaborators to support both diagnosis and long-term management of the disease.

Suggested Citation

  • Abeer Y. Al-Hyari & Ahmad M. Al-Taee & Majid A. Al-Taee, 2014. "Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 9(4), pages 1-12, October.
    • Handle: RePEc:igg:jitwe0:v:9:y:2014:i:4:p:1-12
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijitwe.2014100101
    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:jitwe0:v:9:y:2014:i:4:p:1-12. 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.