Artificial neural networks and robust Bayesian classifiers for risk stratification following uncomplicated myocardial infarction

Abstract

Objective To compare artificial neural networks (ANN) and robust Bayesian classifiers (RBC) in predicting outcome following acute myocardial infarction (AMI). Methods Clinical, exercise ECG and stress echo variables by 496 patients with AMI were used to predict the cumulative end-point of cardiac death, nonfatal reinfarction and unstable angina. Revascularized patients were censored. Short (200 days)-, medium (400 days)- and long (1000 days)-term observation intervals, including 50%, 75% and 90% of the events, respectively, were considered. At each interval, any patient was binary assigned to the “event” or “no event” class. A multilayer feedforward ANN, trained by a back propagation algorithm, was used. RBC, using the leave-one-out technique, were derived. The accuracy of both techniques was compared to the default accuracy (DA) obtained by assigning all subjects to the largest class. Results 14 death, 27 reinfarction and 29 unstable angina were observed during a mean follow-up of 24 [95% confidence interval (CI) 19 to 22] months. The accuracy of ANN and RBC and DA were 70%, 81% and 74% at short, 67%, 73% and 56% at medium and 64%, 68% and 62% at long-term follow-up. Conclusions (1) ANN do not improve the prognostic classification of patients with uncomplicated AMI as compared to RBC. (2) In particular, short-term prognostic accuracy seems insufficient.