IDEAS home Printed from https://ideas.repec.org/a/ids/ijcist/v20y2024i2p111-124.html
   My bibliography  Save this article

Non-invasive prediction mechanism for COVID-19 disease using machine learning algorithms

Author

Listed:
  • Arnav Bhardwaj
  • Hitesh Agarwal
  • Anuj Rani
  • Prakash Srivastava
  • Manoj Kumar
  • Sunil Gupta

Abstract

This paper has focused on developing a model to detect non-diagnostically whether the person is infected with the COVID-19 disease using all relevant symptoms and details mentioned by the person and then comparing it with a pre-defined dataset of positive cases using machine learning. Different models have been developed to predict the same but none of them focused on the detection of COVID-19 based on symptoms. In a developing nation with a huge population, where the diagnostic availability is scarce, just scanning the body temperature will not help in detection of COVID-19 of a particular individual. This paper presents a model that can predict COVID-19 cases without any testing kit to an accuracy of 99.30%, performing better than other similar approaches with objective to put forward a method that can reduce the need of producing testing kits and also the need to wait for hours before we get the results.

Suggested Citation

  • Arnav Bhardwaj & Hitesh Agarwal & Anuj Rani & Prakash Srivastava & Manoj Kumar & Sunil Gupta, 2024. "Non-invasive prediction mechanism for COVID-19 disease using machine learning algorithms," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 20(2), pages 111-124.
    • Handle: RePEc:ids:ijcist:v:20:y:2024:i:2:p:111-124
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=137406
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijcist:v:20:y:2024:i:2:p:111-124. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=58 .

    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.