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Journal of Renal Injury Prevention 2017;6(2):83-87. doi:10.15171/jrip.2017.16
Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other

Original Article

Shahram Tahmasebian 1, Marjan Ghazisaeedi 1 * , Mostafa Langarizadeh 2, Mehrshad Mokhtaran 1, Mitra Mahdavi-Mazdeh 3, Parisa Javadian 4

1 Department of Health Information Management, School of Allied Medical Sciences, Tehran, University of Medical Sciences, Tehran, Iran
2 Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3 Department of Nephrology, Tehran University of Medical Sciences; Research Center of Iranian Tissue Bank, Tehran, Iran
4 Department of Internal Medicine, School of Medicine, Shahrekord University of Medical Sciences; Shahrekord, Iran


*Corresponding author: Marjan Ghazisaeedi,
Email: Ghazimar@tums.ac.ir

Abstract

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients’ medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information.

Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD.

Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data.

Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining)  methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified.

Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD.



Notes

Implication for health policy/practice/research/medical education:

The widespread use of medical information systems and the explosive growth of medical databases for become more efficient have had the needs to traditional data analysis using computer-assisted analysis. Data mining technique was used on the patients’ data to determine the weight and importance of the parameters.

Please cite this paper as: Tahmasebian S, Ghazisaeedi M, Langarizadeh M, Mokhtaran M, Mahdavi-Mazdeh M, Javadian P. Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other. J Renal Inj Prev. 2017;6(2):83-87. DOI: 10.15171/jrip.2017.16.


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Copyright © 2016 The Author(s)
Published by Nickan Research Institute