Prediction of Kidney Graft Rejection Using Artificial Neural Network.
10.4258/hir.2017.23.4.277
- Author:
Leili TAPAK
1
;
Omid HAMIDI
;
Payam AMINI
;
Jalal POOROLAJAL
Author Information
1. Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
- Publication Type:Original Article
- Keywords:
Kidney Transplantation;
Graft Rejection;
Logistic Models;
Neural Networks;
Data Mining
- MeSH:
Causality;
Cold Ischemia;
Creatinine;
Data Mining;
Graft Rejection*;
Humans;
Iran;
Kidney Failure, Chronic;
Kidney Transplantation;
Kidney*;
Logistic Models;
Quality of Life;
Renal Replacement Therapy;
Retrospective Studies;
Risk Factors;
ROC Curve;
Transplants*
- From:Healthcare Informatics Research
2017;23(4):277-284
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR). METHODS: The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection. RESULTS: Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR. CONCLUSIONS: The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.
