1. Coronary Artery Bypass Risk Prediction Using Neural Networks
Richard P Lippmann, PhD, David M Shahian, MD 
The Annals of Thoracic Surgery 
Volume 63, Issue 6, Pages (June 1997)
DOI: /S (97)

2. Fig. 1
Two-layer, multilayer perceptron neural network using random weight initialization and back propagation.
The Annals of Thoracic Surgery  , DOI: ( /S (97) )

3. Fig. 2
Block diagram of risk prediction system with confidence intervals.
The Annals of Thoracic Surgery  , DOI: ( /S (97) )

4. Fig. 3
(A) Receiver operating characteristic curve for committee classifier. Area (C-index) = 76.4%. (B) Fifty superimposed receiver operating characteristic curves generated using bootstrap sampling. Average receiver operating characteristic curve area (AVE AREA) = 75.4% ± 0.3%, suggesting little variability in neural network output secondary to random weight initialization and stochastic descent gradient training.
The Annals of Thoracic Surgery  , DOI: ( /S (97) )

5. Fig. 4
Calibration, by mortality bins, of four classifiers. (MLP = multilayer sigmoid neural network.)
The Annals of Thoracic Surgery  , DOI: ( /S (97) )

6. Fig. 5
Block diagram of bootstrap method for determination of confidence intervals for multilayer perceptron. (MLP = multilayer sigmoid neural network.)
The Annals of Thoracic Surgery  , DOI: ( /S (97) )

7. Fig. 6
Confidence intervals for two-layer multilayer perceptron classifier.
The Annals of Thoracic Surgery  , DOI: ( /S (97) )