IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v7y2025i6p165-171.html
   My bibliography  Save this article

NeuroWise: AI-Based NLP Model for Early Alzheimer’s Detection Using Clinical Text

Author

Listed:
  • Jibran Shar, Faisal Hussain, Imran Ali, Masroor Ali,Abdul Jabbar

    (Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan)

Abstract

Alzheimer's disease (AD) is a background neurodegenerative illness that affects millions of people worldwide. Early diagnosis and management are important for successful intervention and better patient outcomes. This study introduces a method of AD diagnosis using NLP from clinical notes and medical records. Machine learning algorithms are used for symptom classification and prediction from text data, yielding high accuracy and scalability. The suggested technique provides an affordable solution for early diagnosis, allowing increased access to cognitive healthcare.

Suggested Citation

  • Jibran Shar, Faisal Hussain, Imran Ali, Masroor Ali,Abdul Jabbar, 2025. "NeuroWise: AI-Based NLP Model for Early Alzheimer’s Detection Using Clinical Text," International Journal of Innovations in Science & Technology, 50sea, vol. 7(6), pages 165-171, May.
    • Handle: RePEc:abq:ijist1:v:7:y:2025:i:6:p:165-171
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1314/1885
    Download Restriction: no
    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1314
    Download Restriction: no
    ---><---

    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:abq:ijist1:v:7:y:2025:i:6:p:165-171. 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: Iqra Nazeer (email available below). General contact details of provider: .

    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.