title: Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? The Steering Committee of the Cardiac Care Network of Ontario.
abstract: OBJECTIVE
  To compare the abilities of artificial neural network and logistic regression models to predict the risk of in-hospital mortality after coronary artery bypass graft (CABG) surgery.
  
  
  METHODS
  Neural network and logistic regression models were developed using a training set of 4,782 patients undergoing CABG surgery in Ontario, Canada, in 1991, and they were validated in two test sets of 5,309 and 5,517 patients having CABG surgery in 1992 and 1993, respectively.
  
  
  RESULTS
  The probabilities predicted from a fully trained neural network were similar to those of a "saturated" regression model, with both models detecting all possible interactions in the training set and validating poorly in the two test sets. A second neural network was developed by cross-validating a network against a new set of data and terminating network training early to create a more generalizable model. A simple "main effects" regression model without any interaction terms was also developed. Both of these models validated well, with areas under the receiver operating characteristic curves of 0.78 and 0.77 (p > 0.10) in the 1993 test set. The predictions from the two models were very highly correlated (r=0.95).
  
  
  CONCLUSIONS
  Artificial neural networks and logistic regression models learn similar relationships between patient characteristics and mortality after CABG surgery.
authors.affiliation: Institute for Clinical Evaluative Sciences in Ontario, Department of Medicine, Sunnybrook Health Science Centre, University of Toronto, Canada.
authors.forename: J V
authors.initials: JV
authors.lastname: Tu
authors.forename: M C
authors.initials: MC
authors.lastname: Weinstein
authors.forename: B J
authors.initials: BJ
authors.lastname: McNeil
authors.forename: C D
authors.initials: CD
authors.lastname: Naylor
mesh_terms: D000368:Aged; D015897:Comorbidity; D001026:Coronary Artery Bypass / Q000401:mortality*; D004630:Emergencies; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D012720:Severity of Illness Index; D016277:Ventricular Function, Left