An artificial neural network to predict mortality in patients who undergo percutaneous coronary interventions

The objective of this study was to develop a method for identifying patients at increased risk for mortality after percutaneous coronary interventions (PCI). Although the mortality rate after PCI is low (1-2%), the ability to predict the patients with increased risk of mortality can alter the preferred medical strategy and potentially improve the outcome of the patient. We developed a feedforward artificial neural network (ANN) which predicts mortality using 24 variables. The study was based on 812 consecutive patients who underwent PCI between 1.1.95 and 6.30.95 at the Jewish Hospital Heart and Lung Center, Louisville, KY. The predictive power of the network was compared to that of linear discriminant analysis (LDA) using receiver operating characteristics methodology. Our study showed that the performance of the network strongly depended on the choice of the criterion function. Specifically, a modified cross-entropy function worked the best for the network resulting in an ROC area index of Az(ANN)=0.84±0.07 compared to Az(LDA)=0.64±0.12