J Renal Inj Prev. 2017;6(2): 83-87. doi: 10.15171/jrip.2017.16
PMID: 28497080        PMCID: PMC5423289

Original Article

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other

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

Cited by CrossRef: 12

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2- Hamedan F, Orooji A, Sanadgol H, Sheikhtaheri A. Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach. International Journal of Medical Informatics. 2020;138:104134 [Crossref]
3- Akhlaghi A, Langarizadeh M, Rahimzadeh N, Rostami Z. From designing minimum data set to developing kidney transplantation registry in Iran. 2023;12(11):2590 [Crossref]
4- Twarish Alhamazani K, Alshudukhi J, Aljaloud S, Abebaw S, Koundal D. Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives. Computational Intelligence and Neuroscience. 2021;2021:1 [Crossref]
5- Lajoie P, Gaudreault J, Lehoux N, Ali M. A data-driven framework to deal with intrinsic variability of industrial processes: An application in the textile industry. IFAC-PapersOnLine. 2019;52(13):731 [Crossref]
6- Murshid G, Parvez T, Fezal N, Azaz L, Asif M. Data Mining Techniques to Predict Chronic Kidney Disease. IJSRCSEIT. 2019;:1220 [Crossref]
7- Asor J, Catedrilla G, Lerios J. Usage of Classification Algorithm for Extracting Knowledge in Cholesterol Report towards Non-communicable Disease Analysis. JAIT. 2020;11(4):265 [Crossref]
8- Sahu S, Shrivas A. Comparative Study of Classification Models with Genetic Search Based Feature Selection Technique. 2018;9(3):1 [Crossref]