Discovery of significant parameters in kidney dialysis data sets by K-means algorithm

B. V. Ravindra; N. Sriraam; M. Geetha

doi:10.1109/CIMCA.2014.7057843

Discovery of significant parameters in kidney dialysis data sets by K-means algorithm

B. V. Ravindra, N. Sriraam, M. Geetha

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.

Original language	English
Title of host publication	Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	452-454
Number of pages	3
ISBN (Electronic)	9781479965465
DOIs	https://doi.org/10.1109/CIMCA.2014.7057843
Publication status	Published - 10-03-2014
Externally published	Yes
Event	2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014 - Bangalore, India Duration: 21-11-2014 → 22-11-2014

Conference

Conference	2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014
Country/Territory	India
City	Bangalore
Period	21-11-14 → 22-11-14

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1109/CIMCA.2014.7057843

Cite this

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abstract = "The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.",

author = "Ravindra, {B. V.} and N. Sriraam and M. Geetha",

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Ravindra, BV, Sriraam, N & Geetha, M 2014, Discovery of significant parameters in kidney dialysis data sets by K-means algorithm. in Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014., 7057843, Institute of Electrical and Electronics Engineers Inc., pp. 452-454, 2014 International Conference on Circuits, Communication, Control and Computing, I4C 2014, Bangalore, India, 21-11-14. https://doi.org/10.1109/CIMCA.2014.7057843

Discovery of significant parameters in kidney dialysis data sets by K-means algorithm. / Ravindra, B. V.; Sriraam, N.; Geetha, M.

Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 452-454 7057843.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Sriraam, N.

AU - Geetha, M.

PY - 2014/3/10

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N2 - The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.

AB - The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.

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BT - Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014

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Discovery of significant parameters in kidney dialysis data sets by K-means algorithm

Abstract

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All Science Journal Classification (ASJC) codes

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