IDEAS home Printed from https://ideas.repec.org/a/igg/jehmc0/v8y2017i4p38-60.html
   My bibliography  Save this article

Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease

Author

Listed:
  • Sakshi Takkar

    (Lovely Professional University, Phagwara, India)

  • Aman Singh

    (Department of Computer Science and Engineering, Lovely Professional University, Phagwara, India)

  • Babita Pandey

    (Department of Computer Applications, Lovely Professional University, Phagwara, India)

Abstract

Liver diseases represent a major health burden worldwide. Machine learning (ML) algorithms have been extensively used to diagnose liver disease. This study accordingly aims to employ various individual and integrated ML algorithms on distinct liver disease datasets for evaluating the diagnostic performances, to integrate dimensionality reduction method with the ML algorithms for analyzing variation in results, to find the best classification model and to analyze the merits and demerits of these algorithms. KNN and PCA-KNN emerged to be the top individual and integrated models. The study also concluded that one specific algorithm can't show best results for all types of datasets and integrated models not always perform better than the individuals. It is observed that no algorithm is perfect and performance of an algorithm totally depends on the dataset type and structure, its number of observations, its dimensions and the decision boundary.

Suggested Citation

  • Sakshi Takkar & Aman Singh & Babita Pandey, 2017. "Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 8(4), pages 38-60, October.
    • Handle: RePEc:igg:jehmc0:v:8:y:2017:i:4:p:38-60
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.2017100103
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aritra Pan & Shameek Mukhopadhyay & Subrata Samanta, 2022. "Liver Disease Detection: Evaluation of Machine Learning Algorithms Performances With Optimal Thresholds," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(2), pages 1-19, April.

    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:jehmc0:v:8:y:2017:i:4:p:38-60. 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.