Volume 79, Issue 6 (September 2021)                   Tehran Univ Med J 2021, 79(6): 459-467 | Back to browse issues page

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Moeinzadeh F, Rouhani M H, Mortazavi M, Sattari M. Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques. Tehran Univ Med J. 2021; 79 (6) :459-467
URL: http://tumj.tums.ac.ir/article-1-11335-en.html
1- Isfahan Kidney Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
2- Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran.
3- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. , msattarimng.mui@gmail.com
Abstract:   (469 Views)
Background: Millions of deaths occur around the world each year due to lack of access to appropriate treatment for chronic kidney disease patients. Given the importance and mortality rate of this disease, early and low-cost prediction is very important. The researchers intend to identify chronic kidney disease through the optimal combination of techniques used in different stages of data mining.
Methods: This cross-sectional research was conducted from February 1999 to May 2014. The used data set included 4145 samples and 32 attributes, where Each sample corresponded to a patient and each attribute corresponded to the demographic and clinical traits. There were several eligibility criteria for the patients for clinical testing. These criteria for the clinical testing included having 18 years of age and older, living in Isfahan city, willing to participate in the study, lack of fever and cold during laboratory tests, no strenuous exercise 48 hours before laboratory tests, and fasting. Individuals who had an incomplete questionnaire or were unwilling to perform accurate tests were excluded from the study. The target variable is kidney disease, the values of which include sick and healthy. Four data mining techniques have been used in the dataset. These techniques are support vector machine (SVM), random forest (RF), artificial neural network (ANN) and Chi-square automatic interaction detection (CHAID).
Results: Accuracy is the evaluation criteria for comparing available data mining methods. Based on the accuracy criterion, the support vector machine performed better than other techniques (random forest, neural network and CHAID). The best rule is that if the patients consume salt in their diet, their age is between 50 and 69, and they have diabetes. they are 82% more likely to develop chronic kidney disease.
Conclusion: The derived rules also showed that if we use salt and we have diabetes, we are at the risk of developing chronic kidney disease. Moreover, having diabetes can increase the risk of mortality in chronic kidney patients. Aged people should also be more careful about getting chronic kidney disease. Because, they are more prone to develop chronic kidney disease.
 
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