IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-031-75329-9_34.html
   My bibliography  Save this book chapter

Enhancing Diabetes Risk Prediction with Hybrid Machine Learning Models

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • Sahar Echajei

    (Ben M’sik - Hassan II University of Casablanca)

  • Hanane Ferjouchia

    (Ben M’sik - Hassan II University of Casablanca)

  • Mostafa Rachik

    (Ben M’sik - Hassan II University of Casablanca)

Abstract

This paper explores the integration of causal inference with machine learning (ML) to enhance early diagnosis and effective management of diabetes. By leveraging advanced techniques such as data preprocessing, causal analysis, evaluation of variable importance, feature engineering, and hyperparameter optimization, we develop a predictive model using a Stacking ensemble that combines multiple base models. Initial results demonstrate significant improvements in model performance, suggesting that this integrated approach offers a promising direction for diabetes management.

Suggested Citation

  • Sahar Echajei & Hanane Ferjouchia & Mostafa Rachik, 2024. "Enhancing Diabetes Risk Prediction with Hybrid Machine Learning Models," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 310-318, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_34
    DOI: 10.1007/978-3-031-75329-9_34
    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 search for a similarly titled item that would be available.

    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:lnichp:978-3-031-75329-9_34. 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.