<title>Using neural networks to predict the risk of cardiac bypass operations</title>

Abstract

Experiments demonstrate that sigmoid multilayer perceptron (MLP) networks provide slightly better risk prediction than conventional logistic regression and Bayesian models when used to predict the risk of death using a data base of 41,385 patients who underwent coronary artery bypass operations in 1993. MLP networks with no hidden layers (single-layer MLPs), networks with one hidden layer (two-layer MLPs), and networks with two hidden layers (three-layer MLPs) were trained using stochastic gradient descent with early stopping. All prediction techniques used the same input features and were evaluated by training on 20,698 patients and testing on a separate 20,687 patients. Receiver operating characteristic (ROC) curve areas for predicting mortality were roughly 75% for all classifiers. Risk stratification or accuracy of posterior probability prediction was slightly better with three-layer MLP networks which did not inflate risk for high-risk patients. Simple approaches were developed to calculate effective odds ratios for MLP networks and to generate confidence intervals for MLP risk predictions using an auxiliary `confidence MLP.' The confidence MLP is trained to reproduce confidence intervals that were generated during training using the outputs of 50 MLP networks trained with different bootstrap samples.