Chronic Kidney Disease Prediction
Kumar Gaurav1 , Darshana A. Naik2 , Visesh Kumar Jaiswal3 , anollas M4 , Ankitha V5
Section:Research Paper, Product Type: Journal Paper
Volume-7 ,
Issue-4 , Page no. 1065-1069, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.10651069
Online published on Apr 30, 2019
Copyright © Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V, “Chronic Kidney Disease Prediction,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1065-1069, 2019.
MLA Style Citation: Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V "Chronic Kidney Disease Prediction." International Journal of Computer Sciences and Engineering 7.4 (2019): 1065-1069.
APA Style Citation: Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V, (2019). Chronic Kidney Disease Prediction. International Journal of Computer Sciences and Engineering, 7(4), 1065-1069.
BibTex Style Citation:
@article{Gaurav_2019,
author = {Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V},
title = {Chronic Kidney Disease Prediction},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {1065-1069},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4167},
doi = {https://doi.org/10.26438/ijcse/v7i4.10651069}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.10651069}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4167
TI - Chronic Kidney Disease Prediction
T2 - International Journal of Computer Sciences and Engineering
AU - Kumar Gaurav, Darshana A. Naik, Visesh Kumar Jaiswal, Manollas M, Ankitha V
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 1065-1069
IS - 4
VL - 7
SN - 2347-2693
ER -
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Abstract
Chronic kidney Disease (CKD), also known as chronic renal disease, which is continuous malfunction of kidney for months or even years. Identified based on the kidney damage or decrease in glomerular filtration rate (GFR). People with CKD are more prone to cardiovascular death than actual kidney failure. CKD is progressively predominant in patients with CVD or factors such as dyslipidemia, diabetes mellitus, hypertension and metabolic disorder. Classification models are built and are called classifiers. These classifiers will group the entered data set information to prominent classes. Chronic kidney disorder means the damage lasts and it only worsens over the period of time if not taken care of properly. This illness commonly known as kidney failure does not have any symptoms specific to the disease also sometimes the symptoms are not present and is diagnosed only by a lab test. The illness is highly diagnosed in the age of range 19-40 and higher in ages >40, here the waste starts accumulating over time as the Glomerular Filtration Rate(GFR) decreases overtime leading to increase in impurity of the blood. In this paper we are predicting the severity of kidney stage with the help of patients test report and using prediction algorithms, also we are doing a cross validation using C4.5 algorithms.
Key-Words / Index Term
Naïve Bayes, C4.5, Chronic Kidney Disease, Cross-Validation, Pre-processing
References
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