Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MOEINZADEH FIROUZEH; ROUHANI MOHAMMAD HOSSEIN; MORTAZAVI MOJGAN; SATTARI MOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Tehran University Medical Journal Year:2021 | Volume:79 | Issue:6 Page(s): 459-467

Download Full-Text

Persian Verion

View:

278

Download:

Cites:

Information Journal Paper

Title

Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques

Author(s)

MOEINZADEH FIROUZEH | ROUHANI MOHAMMAD HOSSEIN | MORTAZAVI MOJGAN | SATTARI MOHAMMAD | Issue Writer Certificate

Keywords

chronic kidney disease

data mining

prediction

Abstract

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.

Cites

No record.

References

No record.

Cite

APA: Copy

MOEINZADEH, FIROUZEH, ROUHANI, MOHAMMAD HOSSEIN, MORTAZAVI, MOJGAN, & SATTARI, MOHAMMAD. (2021). Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), 79(6 ), 459-467. SID. https://sid.ir/paper/964209/en

Vancouver: Copy

MOEINZADEH FIROUZEH, ROUHANI MOHAMMAD HOSSEIN, MORTAZAVI MOJGAN, SATTARI MOHAMMAD. Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ)[Internet]. 2021;79(6 ):459-467. Available from: https://sid.ir/paper/964209/en

IEEE: Copy

FIROUZEH MOEINZADEH, MOHAMMAD HOSSEIN ROUHANI, MOJGAN MORTAZAVI, and MOHAMMAD SATTARI, “Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques,” TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), vol. 79, no. 6 , pp. 459–467, 2021, [Online]. Available: https://sid.ir/paper/964209/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops