Classification and disease probability prediction via machine learning programming based on multi-GPU cluster MapReduce system

This paper described the nascent filed of big health data classification and disease probability prediction based on multi-GPU cluster MapReduce platform. Firstly, we presented a novel optimization-based multi-GPU cluster MapReduce system (gcMR) which is general purpose and suitable for processing big health data. Secondly, we proposed a new method IVP-SVM to solve the problem of big health data classification and disease probabilistic predictive inaccuracy. To illustrate the power and flexibility of gcMR platform for big health data, applications of a broad class of health big data using IVP-SVM on gcMR platform are described. Experimental results shown that gcMR platform yields an average computing efficiency on different health applications ranging from 1.8- to 13.5-folds by comparing gcMR with other Multi-GPU MapReduce platform. And an accuracy of the proposed IVP-SVM on different health applications is ranging from 85 to 100 %. This provides a motivation for pursuing the use of gcMR and IVP-SVM as a big health data analytical platform and tool, respectively.

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