Data Mining Techniques Used To Predict Chronic Kidney Disease

Authors(5) :-Chithra A G, Chandana B, Darshan R, Harshitha H S, Nasreen Fathima

Chronic kidney disease is a global health issue and area of concern, associated with an increased risk of cardio vascular diseases and chronic renal failure[1]. It is a symptom where kidney fails to filter toxic wastes from the body, which results in decomposition of wastes in human body and leads to dangerous results. The two main causes of this disease are diabetes and high blood pressure, which are responsible for up to two-third of cause[5]. The healthcare sector has huge medical data but the main difficulty is how to cultivate the existing information into useful practices[3]. To unfold this hurdle the concept of data mining is best suited. The main objective of this paper is to use data mining technique such as random forest, RBF, K-means clustering and Naïve Bayes for the prediction of chronic kidney disease and to summarize the efficiency of Naïve Bayes method by generating suitable results.

Authors and Affiliations

Chithra A G
Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
Chandana B

Darshan R
Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
Harshitha H S

Nasreen Fathima

Data mining, Classification, Chronic Kidney disease, Random forest, RBF, K-means clustering.

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Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 54-58
Manuscript Number : CSEIT184612
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Chithra A G, Chandana B, Darshan R, Harshitha H S, Nasreen Fathima, "Data Mining Techniques Used To Predict Chronic Kidney Disease", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.54-58, May-June.2018
URL : http://ijsrcseit.com/CSEIT184612

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