IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v25y2022i4d10.1007_s10729-022-09611-6.html
   My bibliography  Save this article

A clinical decision-support system for dengue based on fuzzy cognitive maps

Author

Listed:
  • William Hoyos

    (Universidad de Córdoba
    Universidad EAFIT)

  • Jose Aguilar

    (Universidad EAFIT
    Universidad de Los Andes
    Universidad de Alcalá)

  • Mauricio Toro

    (Universidad EAFIT)

Abstract

Dengue is a viral infection widely distributed in tropical and subtropical regions of the world. Dengue is characterized by high fatality rates when the diagnosis is not made promptly and effectively. To aid in the diagnosis of dengue, we propose a clinical decision-support system that classifies the clinical picture based on its severity, and using causal relationships evaluates the behavior of the clinical and laboratory variables that describe the signs and symptoms related to dengue. The system is based on a fuzzy cognitive map that is defined by the signs, symptoms and laboratory tests used in the conventional diagnosis of dengue. The evaluation of the model was performed on datasets of patients diagnosed with dengue to compare the model with other approaches. The developed model showed a good classification performance with 89.4% accuracy and could evaluate the behaviour of clinical and laboratory variables related to dengue severity (it is an explainable method). This model serves as a diagnostic aid for dengue that can be used by medical professionals in clinical settings.

Suggested Citation

  • William Hoyos & Jose Aguilar & Mauricio Toro, 2022. "A clinical decision-support system for dengue based on fuzzy cognitive maps," Health Care Management Science, Springer, vol. 25(4), pages 666-681, December.
    • Handle: RePEc:kap:hcarem:v:25:y:2022:i:4:d:10.1007_s10729-022-09611-6
    DOI: 10.1007/s10729-022-09611-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-022-09611-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-022-09611-6?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.

    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:kap:hcarem:v:25:y:2022:i:4:d:10.1007_s10729-022-09611-6. 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.