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A voluminous amount of data is generated because of the inexorably widespread proliferation of electronic data maintained using the electronic health records (EHRs). Medical health facilities have great potential to discern patterns from this data and utilize them in diagnosing specific diseases or predicting the outbreak of an epidemic. This discerning 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 κappa statistics analyses. 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 sensitive data.

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