IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v74y2023i1p81-92.html
   My bibliography  Save this article

Multi-criteria appraisal recommendation

Author

Listed:
  • Chao Fu
  • Qianshan Zhan
  • Leilei Chang
  • Weiyong Liu
  • Shanlin Yang

Abstract

Generating the overall assessments of cases from their observations on multiple criteria when large volumes of historical data have been accumulated is a key issue. This study, therefore, developed the framework of multi-criteria appraisal recommendation (MCAR). Five strategies belonging to three categories were designed to recommend the overall appraisals of new cases from their observations on multiple criteria based on relevant historical data. The proposed framework’s basic conditions and key issues were presented to widen its application. The framework was then used to generate the diagnostic recommendations for thyroid nodules from their observations based on the historical examination reports of six radiologists. The experimental results indicated that different strategies are appropriate for different radiologists, and no single strategy was found to be the most appropriate for all considered radiologists. The five strategies were compared with four representative machine learning models to highlight their performances and interpretabilities using the historical examination reports of the radiologists.

Suggested Citation

  • Chao Fu & Qianshan Zhan & Leilei Chang & Weiyong Liu & Shanlin Yang, 2023. "Multi-criteria appraisal recommendation," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(1), pages 81-92, January.
  • Handle: RePEc:taf:tjorxx:v:74:y:2023:i:1:p:81-92
    DOI: 10.1080/01605682.2021.2023674
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2021.2023674
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01605682.2021.2023674?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:tjorxx:v:74:y:2023:i:1:p:81-92. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.