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Proceedings Paper

Using neural networks to predict the risk of cardiac bypass operations
Author(s): Richard P. Lippmann; Linda Kukolich
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Paper 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.

Paper Details

Date Published: 6 April 1995
PDF: 10 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205180
Show Author Affiliations
Richard P. Lippmann, MIT Lincoln Lab. (United States)
Linda Kukolich, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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