Predicting Early Transplant Failure: Neural Network Versus Logistic Regression Models

Abstract

Cox's proportional hazard model or logistic regression model has been the classical mathematical approach to predict transplant results, but artificial neural networks may offer better results. In order to compare both methods, a logis- tic regression and a neural network model were generated to predict early transplant failure assessed at 90 days. Methods: Medical charts from 701 liver transplant patients were used as generation cohort, collecting variables from do- nor, recipient and operative data. The discrimination capacity of the models was measured through the area under their ROC curves. Models were validated by applying them to a second cohort of 170 patients (validation cohort), although af- terwards it was enlarged to 246 patients in order to increase statistical power. Results: For the generation sample, ROC curves were 75% for logistic regression and 96% for neural network (� 2 = 44,60. p<0,00001). Applied to the whole validation sample these values dropped to 68.7 % for logistic regression and 69.9 % for neural network (� 2 = 0.026. p: 0,87). However, when models where applied to the validation cohort in cumulative groups of 50 patients two aspects became evident: 1) predictions worsened for patients who were more distant in time from the generation cohort; 2) for the first hundred patients in validation cohort, neural network was clearly superior to logistic re- gression model (93 % vs 76 %; � 2 = 10.52. p:0,001). Conclusions: Our results suggest that, provided with the same information and for a limited period of time, neural net- works may offer better diagnostic performances than with logistic regression models.