Adaptive error approximate data reconciliation technique for healthcare framework

S. Satheesh Kumar () and Manjula Sanjay Koti ()
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S. Satheesh Kumar: REVA University
Manjula Sanjay Koti: Dayananda Sagar Academy of Technology and Management

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 32, 356-366

Abstract: 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.

Keywords: Data reconciliation; Healthcare analysis; Internet of things (IoT); Adaptive error; Cloud layer (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01744-9

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