Background and Objective: Coronary artery disease is one of the most
prevalent causes of death. A coronary artery
bypass surgery is a common treatment for this disease.
In addition, renal dysfunction can lead to increased mortality and post-operative
complications. This study aimed to identify the most important factors
influencing the mortality of patients who suffer from coronary artery disease
and to introduce a classification approach according to Classification Tree
(CART) model for predicting the mortality from this disease.    Materials and Methods: This research was conducted based on the information
gathered from a cross-sectional study on 1390 patients (except
dialysis-dependent) who undergone coronary artery bypass grafting, admitted to
Cardiology ward of Shariati hospital during the years 2007-2010. The ordinary
logistic regression model and a classification tree were utilized for
predicting the probability of death in these patients. The SPSS version 18.0
and CART version 6.0 were used for data analysis.  Results: In this study, the classification tree model (CART)
resulted in an accuracy of 90%. The patients with renal insufficiency,
intra-aortic balloon pump placement during and after surgery, prolonged
ventilation, and perfusion time over 160 were shown as the high-risk groups,
while those patients with heart ventricular post-operative complications
regarded as the medium-risk group. The sensitivity and specificity indices for
this model were 82% and 89%, respectively, while it was 80.4% and 88%,
respectively, for logistic model.   Conclusion: In the present study, the logistic and decision
tree models led to nearly similar results, however, the decision tree model
seemed to be more accurate. The IABP (Intra-Aortic
Balloon Pump < /font>) was the most effective factor for mortality. The
mortality rate due to this factor during and after surgery for all patients was
19% and 54.1%, respectively.