An Efficient Framework for Analyzing Real Time Distributed Health Care Data
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Date
2015
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Abstract
Real time monitoring o f vital health parameters assure not only early detection and prediction o f the disease but
also reduce the health care costs. The technological advancements prevailing today make it possible to sense, collect,
transmit, store and analyze the voluminous, heterogeneous big data. This data could be mined to derive valuable and
interesting patterns. However, extracting knowledge from big data poses a number o f challenges that are to be addressed. In
this paper, we propose an efficient framework for analyzing real time distributed health care data, considering the
computation time and space complexities. This framework is designed to discover knowledge from a reduced database with
selected features, using a machine learning algorithm. That is. the proposed framework (i) Segments/groups the clinical
data records based on similarity (ii) Selects the prominent features that are required for smarter analysis (Hi) Discovers
patterns using a machine learning algorithm. The discovered patterns provide valuable information required for real time
diagnosis, prognosis and treatment planning.