Research Article

The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods

Volume: 4 Number: Special Issue-1 December 26, 2016
EN

The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods

Abstract

Chronic kidney disease is a prolonged disease that damages the kidneys and prevents the normal duties of the kidneys. This disease is diagnosed with an increase of urinary albumin excretion lasting more than three months or with significant reduction in a kidney functions. Chronic kidney disease can lead to complications such as high blood pressure, anemia, bone disease and cardiovascular disease. In this study we have been investigated to determine the factors that decisive for early detection of chronic kidney disease, launching early patients treatment processes, prevent complications resulting from the disease and predict of disease.  The study aimed diagnosis and prediction of disease using the data set that composed of data of 250 patients with chronic kidney disease and 150 healthy people. First, the chronic kidney disease data was classified with machine learning algorithms and then training and test results were analysed.  The estimation results of chronic kidney disease were compared with similar data and studies.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Enes Çelik
Türkiye

Muhammet Atalay
KIRKLARELİ ÜNİVERSİTESİ
Türkiye

Adil Kondiloglu This is me
BEYKENT ÜNİVERSİTESİ
Türkiye

Publication Date

December 26, 2016

Submission Date

November 14, 2016

Acceptance Date

December 1, 2016

Published in Issue

Year 2016 Volume: 4 Number: Special Issue-1

APA
Çelik, E., Atalay, M., & Kondiloglu, A. (2016). The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 27-31. https://doi.org/10.18201/ijisae.265967
AMA
1.Çelik E, Atalay M, Kondiloglu A. The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):27-31. doi:10.18201/ijisae.265967
Chicago
Çelik, Enes, Muhammet Atalay, and Adil Kondiloglu. 2016. “The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods”. International Journal of Intelligent Systems and Applications in Engineering 4 (Special Issue-1): 27-31. https://doi.org/10.18201/ijisae.265967.
EndNote
Çelik E, Atalay M, Kondiloglu A (December 1, 2016) The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 27–31.
IEEE
[1]E. Çelik, M. Atalay, and A. Kondiloglu, “The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 27–31, Dec. 2016, doi: 10.18201/ijisae.265967.
ISNAD
Çelik, Enes - Atalay, Muhammet - Kondiloglu, Adil. “The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 1, 2016): 27-31. https://doi.org/10.18201/ijisae.265967.
JAMA
1.Çelik E, Atalay M, Kondiloglu A. The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:27–31.
MLA
Çelik, Enes, et al. “The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, Dec. 2016, pp. 27-31, doi:10.18201/ijisae.265967.
Vancouver
1.Enes Çelik, Muhammet Atalay, Adil Kondiloglu. The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods. International Journal of Intelligent Systems and Applications in Engineering. 2016 Dec. 1;4(Special Issue-1):27-31. doi:10.18201/ijisae.265967

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