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Privacy-preserving clinical decision support system using gaussian kernel-based classification

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posted on 02.02.2017 by Yogachandran Rahulamathavan, Suresh Veluru, Raphael C.-W. Phan, Jonathon Chambers, Muttukrishnan Rajarajan
A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

History

School

  • Loughborough University London

Published in

IEEE Journal of Biomedical and Health Informatics

Volume

18

Issue

1

Pages

56 - 66

Citation

RAHULAMATHAVAN, Y. ... et al, 2013. Privacy-preserving clinical decision support system using gaussian kernel-based classification. IEEE Journal of Biomedical and Health Informatics, 18 (1), pp. 56-66.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2013

Notes

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

2168-2194

Language

en

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