An Efficient Big Data Storage for Handling Kidney Failures Datasets Using E-Health Platform Integration |
Author(s): |
| G Ben Sandra , jeppiaar engineering college; V.L Jyothi, jeppiaar engineering college |
Keywords: |
| HCP, Radial basis function neural network, Hadoop |
Abstract |
|
Big data is an encompassing difficult or complex large datasets to process on traditional large scale data processing. The main confront of big data processing incorporates the extraction of significant data, from a high dimensionality of a wide assortment of medicinal information by empowering examination, disclosure and elucidation. These data are a useful tool for helping to understand disease and to formulate predictive models in different areas and support different tasks, such as triage, evaluation of treatment, and monitoring. In this work, based on a predictive model using the Distributed radial basis function neural network (DRBFNN) to aiming the estimation of kidney failures is presented. The proposed method exposed appropriateness to sustain patient & health care professionals (HCP) on clinical decisions and practices. |
Other Details |
|
Paper ID: IJSRDV3I120261 Published in: Volume : 3, Issue : 12 Publication Date: 01/03/2016 Page(s): 618-622 |
Article Preview |
|
|
|
|
