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International Journal of Organ Transplantation Medicine، جلد ۸، شماره ۲، صفحات ۰-۰
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression
چکیده انگلیسی مقاله Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008–2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%; 85.9%, 76.9%, 97.3%, and 26.1%; and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | M Nematollahi
Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran


| R Akbari
School of Computer Engineering & IT, Shiraz University of Technology, Shiraz, Iran


| S Nikeghbalian
School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran


| C Salehnasab
Shiraz University of Medical Sciences, Shiraz, Iran
نشانی اینترنتی http://www.ijotm.com/ojs/index.php/IJOTM/article/view/229
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/221/article-221-807886.pdf
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Short Communications
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