Chronic Kidney Disease Analysis using Data Mining

Authors(2) :-Sunil D, Prof. B. P. Sowmya

Data mining has been a current trend for attaining Diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association Analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease (CKD) using classification techniques Like Naive Bayes and Artificial Neural Network (ANN). The Experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.

Authors and Affiliations

Sunil D
Department of Master of Computer Application, PES College of Engineering, Mandya, Karnataka, India
Prof. B. P. Sowmya
Department of Master of Computer Application, PES College of Engineering, Mandya, Karnataka, India

Data mining, Classification, Chronic Kidney, disease, Naive Bayes, Artificial Neural Network.

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 508-511
Manuscript Number : CSEIT1724100
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sunil D, Prof. B. P. Sowmya, "Chronic Kidney Disease Analysis using Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.508-511, July-August-2017.
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