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:: Volume 19, Issue 2 (بهار 1396، شماره 19 (2). 1396) ::
Koomesh 1396, 19(2): 253-262 Back to browse issues page
Estimating survival rate of kidney transplants by using data mining
Leila Shahmoradi , Mostafa Langarizadeh , Gholamreza Pourmand , Ph. D Aghsaei fard , Alireza Borhani
Abstract:   (5759 Views)
Introduction: todays, kidney failure is one of the costly problems of human society and use of renal replacement therapy is increasing in the world and Iran. Survival analysis is one of the fields in medical prognosis and data mining is a process of discovering unknown relationship and is a useful pattern from data and is known as a highly efficient method in survival analysis. Conclusively, the purpose of this study is predicting the survival of the kidney transplant patient's according to variables before kidney transplant. Materials and Methods: In order to identify important factors for predicting survival in kidney transplant, informative requirements assessment was done by using self-designed questionnaire. Then, obtained information from the analysis of questionnaire was reviewed and data from 513 medical record of kidney patient in Sina Urology Research Center was extracted. Ultimately, by applying CRISP methodology, data mining was done by IBM SPSS Modeler 14.2 and C.5 algorithm. Results: In this study, BMI, ESRD and dialysis time were evaluated as the most effective factors in survival kidney transplant and extracted rules from the model can be used for predicting the survival of the transplanted kidney before the surgery. Accuracy rate of this model was estimated at 96.77%. Conclusion: The high accuracy rate of C5.0 model shows the power of it in survival prediction. Furthermore, the most effective kidney transplant survival factors were identified and kidney transplanted survival of a new patient with distinctive features, can be predicted
Keywords: Data Mining, Survival Analysis, Kidney Transplantation, Prediction, CRISP Methodology
Full-Text [PDF 875 kb]   (1855 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/04/19 | Accepted: 2016/12/11 | Published: 2017/03/15
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Shahmoradi L, Langarizadeh M, Pourmand G, Aghsaei fard P D, Borhani A. Estimating survival rate of kidney transplants by using data mining. Koomesh 1396; 19 (2) :253-262
URL: http://koomeshjournal.semums.ac.ir/article-1-3310-en.html


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Volume 19, Issue 2 (بهار 1396، شماره 19 (2). 1396) Back to browse issues page
کومش Koomesh
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