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An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining

An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining

Musavir Hassan, Muheet Ahmed Butt, Majid Zaman
Copyright: © 2021 |Volume: 12 |Issue: 6 |Pages: 23
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799867517|DOI: 10.4018/IJEHMC.20211101.oa8
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MLA

Hassan, Musavir, et al. "An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining." IJEHMC vol.12, no.6 2021: pp.1-23. http://doi.org/10.4018/IJEHMC.20211101.oa8

APA

Hassan, M., Butt, M. A., & Zaman, M. (2021). An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-23. http://doi.org/10.4018/IJEHMC.20211101.oa8

Chicago

Hassan, Musavir, Muheet Ahmed Butt, and Majid Zaman. "An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.6: 1-23. http://doi.org/10.4018/IJEHMC.20211101.oa8

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Abstract

As the voluminous amount of data is generated because of inexorably widespread proliferation of electronic data maintained using the Electronic Health Records (EHRs). Medical health facilities have great potential to discern the patterns from this data and utilize them in diagnosing a specific disease or predicting outbreak of an epidemic etc. This discern of patterns might reveal sensitive information about individuals and this information is vulnerable to misuse. This is, however, a challenging task to share such sensitive data as it compromises the privacy of patients. In this paper, a random forest-based distributed data mining approach is proposed. Performance of the proposed model is evaluated using accuracy, f-measure and appa statistics analysis. Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful healthcare knowledge in the form of local models without revealing and sharing of sensitive data.