Abstract

Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL). FL maintains patient privacy and data localization by spreading only model parameters, therefore enabling training models on remote datasets. The combination of Partially Homomorphic Cryptosystem (PHC) and Residual Learning based Deep Belief Network (RDBN) ensures an accurate and safe classification of patient physiological data. Experimental results show that the proposed method is successful in maintaining the diagnostic accuracy over numerous healthcare institutions and protecting privacy. The results show that the RDBN and PHC computations requires around 1000 ms and 150 ms, respectively for classification and privacy; the data transmission from the user to server and from server to user is 5 MB and 4 MB, respectively. Finally with a 30% reduction in overhead, the proposed approach offers an average increase in classification accuracy of 10% over multiple datasets.

Details

Title
A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning
Author
Markkandan, S. 1 ; Bhavani, N. P. G. 2 ; Nath, Srigitha S. 3 

 Vellore Institute of Technology, School of Electronics Engineering (SENSE), Chennai, India (GRID:grid.412813.d) (ISNI:0000 0001 0687 4946) 
 Saveetha Institute of Medical and Technical Sciences, Saveetha University, Department of ECE, Saveetha School of Engineering, Chennai, India (GRID:grid.412431.1) (ISNI:0000 0004 0444 045X) 
 Saveetha Engineering College, ECE, Thandalam, Chennai, India (GRID:grid.252262.3) (ISNI:0000 0001 0613 6919) 
Pages
22354
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3110578323
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.