IDEAS home Printed from https://ideas.repec.org/a/plo/pdig00/0000530.html
   My bibliography  Save this article

Diabetes-focused food recommender system (DFRS) to enabling digital health

Author

Listed:
  • Esmael Ahmed
  • Mohammed Oumer
  • Medina Hassan

Abstract

The integration of digital health technologies into diabetes management has shown the potential to improve patient outcomes by providing personalized dietary recommendations. This study aims to develop and evaluate the Diabetes-Focused Food Recommender System (DFRS), a system designed to assist individuals with diabetes in making informed food choices. Using a combination of advanced machine learning algorithms, nutrition science, and digital health technologies, DFRS generates personalized recommendations tailored to individual needs. The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. Hyperparameter tuning and rigorous performance evaluation were conducted to optimize system accuracy. The results demonstrate that after optimization, GNN achieved an accuracy of 94 percent, significantly enhancing the precision of dietary recommendations. Clinical validation of the system showed a reduction in HbA1c levels, glycemic variability, and incidents of hyper- and hypoglycemia. Therefore, DFRS has proven to be an effective tool for improving dietary management in diabetes care, and its integration into clinical workflows offers the potential to enhance health outcomes and streamline healthcare delivery.Author summary: In this research, we introduce the Diabetes-Focused Food Recommender System (DFRS), a digital health solution aimed at improving dietary management for individuals with diabetes. This study responds to the challenges diabetes patients face in making informed dietary choices due to the overwhelming amount of information and personal variability. By leveraging advanced machine learning algorithms alongside nutrition science, DFRS provides personalized dietary recommendations tailored to individual needs. Our development process included rigorous optimization to ensure the system’s adaptability and effectiveness within clinical settings. We found that DFRS significantly enhances glycemic control and reduces diabetes-related complications. Continuous feedback from healthcare professionals and patients informed its refinement, ensuring it meets the needs of real-world users. DFRS is a notable advancement in personalized medicine, empowering users to make informed dietary decisions that can lead to improved health outcomes. Through sustained innovation, DFRS has the potential to transform dietary management and enhance the quality of life for individuals living with chronic conditions like diabetes.

Suggested Citation

  • Esmael Ahmed & Mohammed Oumer & Medina Hassan, 2025. "Diabetes-focused food recommender system (DFRS) to enabling digital health," PLOS Digital Health, Public Library of Science, vol. 4(2), pages 1-10, February.
  • Handle: RePEc:plo:pdig00:0000530
    DOI: 10.1371/journal.pdig.0000530
    as

    Download full text from publisher

    File URL: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000530
    Download Restriction: no

    File URL: https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000530&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pdig.0000530?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
    ---><---

    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:plo:pdig00:0000530. 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: digitalhealth (email available below). General contact details of provider: https://journals.plos.org/digitalhealth .

    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.