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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.