IDEAS home Printed from https://ideas.repec.org/a/ijs/ijsrse/v11y2024i2id34.html

Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning

Author

Listed:
  • Sourabh Pawar, Pranav More
  • Tejas Pawar
  • Priti Rathod

Abstract

Heart complaint is a major global health concern, especially in prognosticating cardiovascular issues. Machine literacy (ML) and the Internet of effects (IoT) offer new ways to dissect healthcare data. still, current exploration lacks depth in using ML for heart complaint vaticination. To fill this gap, we propose a unique system that uses ML to identify crucial features for better heart complaint vaticination delicacy. Our model combines colorful features and bracket ways to achieve an delicacy of 88.7 in prognosticating heart complaint, with the cold-blooded arbitrary timber and direct model (HRFLM) proving particularly effective. This study advances heart complaint discovery by integrating ML and IoT technologies.

Suggested Citation

  • Sourabh Pawar, Pranav More & Tejas Pawar & Priti Rathod, 2024. "Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 11(2), pages 166-171, April.
    • Handle: RePEc:ijs:ijsrse:v11:y2024:i2:id:34
    DOI: 10.32628/IJSRSET2411222
    as

    Download full text from publisher

    File URL: https://ijsrset.com/home/article/view/IJSRSET2411222
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrset.com/home/article/download/IJSRSET2411222/IJSRSET2411222
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRSET2411222?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:v11:y2024:i2:id: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: 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.