IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-598-0_53.html

Machine Learning Methods in Medical Diagnosis

In: Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)

Author

Listed:
  • Nianci You

    (University College London, Crime and Security Science, Department of Crime and Security Science)

Abstract

Incorrect diagnosis can significantly affect outcome of treatment of a patients, which is often caused by cognitive bias of clinicians. This paper summarises development of three common machine learning methods in medical diagnosis -- Artificial Neural Networks (ANNs), Decision Tree and Bayesian Classifier (BC). Development of novel molecular approach can improve the ability of ANN models to accurately classify cancer subtypes such as Small Round Blue Cell Tumors (SRBCTs). Moreover, new medical equipment such as mass spectrometry can assist ANNs model in analysis of ovarian cancer. In order to achieve higher accuracy of Decision Tree in medical diagnosis, Shouman et al. examined different combination of discretization methods and Decision Tree and found that disequal frequency discretization Gain Ratio Decision Tree achieved highest accuracy. In addition, BC is more interpretable than other two classifier models because it can produce probabilistic outputs of the likelihood of a certain diagnosis or outcome.

Suggested Citation

  • Nianci You, 2024. "Machine Learning Methods in Medical Diagnosis," Advances in Economics, Business and Management Research, in: Qiujing Wu & Songsong Liu & Guoliang Wang & Jia Li (ed.), Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), pages 513-519, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-598-0_53
    DOI: 10.2991/978-94-6463-598-0_53
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6463-598-0_53. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.