IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i1d10.1007_s13198-022-01744-9.html
   My bibliography  Save this article

Adaptive error approximate data reconciliation technique for healthcare framework

Author

Listed:
  • S. Satheesh Kumar

    (REVA University)

  • Manjula Sanjay Koti

    (Dayananda Sagar Academy of Technology and Management)

Abstract

Over the past decade, there has been a great deal of study and technical advancement in the area of healthcare services. To be more precise, the Internet of Things (IoT) has showed great promise in linking different medical equipment, sensors, and healthcare experts in order to offer excellent medical services at a distant place. This has improved patient safety, decreased healthcare costs, expanded healthcare service accessibility, and raised operating effectiveness in the healthcare sector. Data reconciliation has played an important role in correcting sensor measurements that can satisfy the requirements of IoT healthcare. Any substantial variation in raw data from the normal that is not caused by the event under monitoring would undermine the analysis. As a result, it is critical to develop strong data reconciliation procedures in order to mitigate the effects of large inaccuracies and deliver correct data. In general, actual data are often corrupted by various large errors. As a result, it is critical to develop strong data reconciliation procedures in order to mitigate the effects of large mistakes and deliver correct data. In this work we proposed cloud-based IoT healthcare data reconciliation (CIH-DR) in which IoT data from different sensors get validated and integrated with destination data at cloud using novel Adaptive error approximate data reconciliation (AEA-DR) model. Two main processes are used in the proposed approach. The first process deals with random measurement variations using a standard data reconciliation method, while the second thread analyses the suggested robust estimator utilizing its objective and impact functions. Through experiment comparison, we showed the efficiency above 90% for the proposed system by using benchmark estimators such as Fair, Welsch, and Cauchy. The results indicate that the proposed robust estimator produces more than 90%, in conjunction with the layered reconciliation structure, is more efficient and viable than the other robust techniques.

Suggested Citation

  • S. Satheesh Kumar & Manjula Sanjay Koti, 2024. "Adaptive error approximate data reconciliation technique for healthcare framework," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 356-366, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01744-9
    DOI: 10.1007/s13198-022-01744-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01744-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-022-01744-9?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
    ---><---

    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:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01744-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.