IDEAS home Printed from https://ideas.repec.org/a/ijs/ijsrse/v12y2025i2id405.html

AI-Powered Early Detection of Diabetes Using Machine Learning on Electronic Health Records

Author

Listed:
  • Sagar Pukale
  • Sudarshan Jagdale
  • Anushaka Bhandari
  • Sourabh Shinde

Abstract

Millions of people worldwide suffer from diabetes, a chronic illness that must be identified early in order to be effectively managed and complications avoided. Conventional diagnostic techniques depend on recurring clinical evaluations, which could postpone prompt action. This study investigates the use of machine learning (ML) methods for early diabetes detection in electronic health records (EHRs). To increase predictive accuracy, we offer an optimized machine learning framework that makes use of the patient's medical history, test results, and lifestyle choices. Results from experiments show that ML models perform better than traditional diagnostic techniques in terms of overall predictive performance, sensitivity, and specificity. Lastly, we go over potential future paths, such as combining explainable AI and deep learning to improve decision-making.

Suggested Citation

  • Sagar Pukale & Sudarshan Jagdale & Anushaka Bhandari & Sourabh Shinde, 2025. "AI-Powered Early Detection of Diabetes Using Machine Learning on Electronic Health Records," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 12(2), pages 578-584, April.
    • Handle: RePEc:ijs:ijsrse:v12:y2025:i2:id:405
    DOI: 10.32628/IJSRSET25122171
    as

    Download full text from publisher

    File URL: https://ijsrset.com/home/article/view/IJSRSET25122171
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrset.com/home/article/download/IJSRSET25122171/IJSRSET25122171
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRSET25122171?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

    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:ijs:ijsrse:v12:y2025:i2:id:405. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsrset.com/home .

    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.