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Authors: Tao Hai, Arindam Sarkar, Muammer Aksoy, Rahul Karmakar, Sarbajit Manna, Amrita Prasad
Published: 03 April 2024 Publication History
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Abstract
Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems.
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Index Terms
Elevating security and disease forecasting in smart healthcare through artificial neural synchronized federated learning
Applied computing
Life and medical sciences
Health care information systems
Health informatics
Computing methodologies
Machine learning
Machine learning approaches
Neural networks
Security and privacy
Human and societal aspects of security and privacy
Security services
Privacy-preserving protocols
Social and professional topics
Computing / technology policy
Medical information policy
Index terms have been assigned to the content through auto-classification.
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Published In
Cluster Computing Volume 27, Issue 6
Sep 2024
1542 pages
Issue’s Table of Contents
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 03 April 2024
Accepted: 10 February 2024
Revision received: 11 January 2024
Received: 02 October 2023
Author Tags
- Federated learning
- Blockchain
- Electronic health record (EHR)
- Internet of Medical Things (IoMT)
- Artificial neural networks (ANNs)
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