An Efficient Framework for Analyzing Real Time Distributed Health Care Data
| dc.category | Conference Proceedings | |
| dc.contributor.author | Sarojini, B | |
| dc.date.accessioned | 2017-03-30T19:33:11Z | |
| dc.date.available | 2017-03-30T19:33:11Z | |
| dc.date.issued | 2015 | |
| dc.department | Computer Science | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/2385 | |
| dc.lang | English | en_US |
| dc.publisher.name | National Seminar on Big Data And Opportunities | en_US |
| dc.publisher.type | National | en_US |
| dc.title | An Efficient Framework for Analyzing Real Time Distributed Health Care Data | en_US |
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