Prediction of Kidney Graft Rejection Using Artificial Neural Network

Abstract

ObjectivesKidney 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).MethodsThe 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.ResultsRecipients' 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.ConclusionsThe 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.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…In order to reinforce the usage and subsequent transformation of AI as well as data-based CDSSs in nephrology, AI, as well as big data, offers the chance to actually source knowledge from expert knowledge and big data and subsequently transform it into some form of intelligent system, which can be applied in risk classification, disease diagnosis, drug discovery, and prognostic evaluation, among some other things. AI might be useful in establishing the type of kidney disease and subsequently help in solving problems related to survival analysis of the patients who have gone through kidney transplants [106][107][108][109][110][111][112][113][114]. Renal biopsy images may be a good data base for application of machine learning algorithms.…”

Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review

“…To do so, the two most highly ranked factors in each of the papers analysed have been selected (two have been selected because Shahmoradi et al, 2016 only has two). Five out of the eight papers mention the factors that influence survival [7,31,32,[35][36][37]. Other factors influencing survival that was mentioned in the papers review are: hypertension, smoking, a history of viral hepatitis B and C, cerebral and peripheral vascular disease, recipient ethnicity category or recipient HCV status, among others.…”

“…The authors in [31] also approached survival from a binary classification, i.e., transplant success or failure. Two predictive techniques are used: a neural network (an MLP) and a logistic regression model.…”

“…For this review, searches were carried out in the following free search engines: PubMed [27], Science Direct [28] and DBLP (Digital Bibliography & Library Project) that is a computer science bibliography website [29]. Luck et al [30] was found in the bibliography when Tapak et al was being reviewed [31].…”

Building artificial intelligence‐based personalized predictive models

“…Tapak et al. predicted irreversible kidney graft rejection in 378 patients using an artificial neural network; however, prediction of the death of a graft in a small cohort has no ongoing clinical practice value for improving graft survival.…”