IDEAS home Printed from https://ideas.repec.org/a/ajp/edwast/v9y2025i5p1932-1941id7349.html
   My bibliography  Save this article

Neural network-based disease prediction: Leveraging symptoms for accurate diagnosis of multiple diseases

Author

Listed:
  • Said Badreddine
  • Tariq Alwadan
  • Asem Omari
  • Hamsa Al Ammari
  • Rashid Ashraf
  • Rachid Moustaquim

Abstract

The development of technology and the availability of patient data have been increasing the leveraging of a data-driven approach to improve diagnostic accuracy. This research introduces a virtual diagnosis program that employs neural networks to predict diseases based on a dataset of 4,920 patients and 132 symptoms. Through exploratory data analysis and correlation analysis, significant associations between symptoms and diseases are identified. The developed system achieves an impressive accuracy rate of 95.6% in diagnosing diseases by utilizing advanced optimization techniques for training the neural network model. This accuracy demonstrates the potential of the program to assist healthcare professionals in making accurate diagnoses, enhancing the precision and efficiency of disease identification. The data-driven approach of this virtual diagnosis tool complements medical expertise, offering valuable support for timely and accurate diagnoses.

Suggested Citation

  • Said Badreddine & Tariq Alwadan & Asem Omari & Hamsa Al Ammari & Rashid Ashraf & Rachid Moustaquim, 2025. "Neural network-based disease prediction: Leveraging symptoms for accurate diagnosis of multiple diseases," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(5), pages 1932-1941.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:5:p:1932-1941:id:7349
    as

    Download full text from publisher

    File URL: https://learning-gate.com/index.php/2576-8484/article/view/7349/2538
    Download Restriction: no
    ---><---

    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:ajp:edwast:v:9:y:2025:i:5:p:1932-1941:id:7349. 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: Melissa Fernandes (email available below). General contact details of provider: https://learning-gate.com/index.php/2576-8484/ .

    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.