Application of step-wise discriminant analysis and Bayesian classification procedure in determining prognosis of acute myocardial infarction.
A retrospective study was carried out to assess the feasibility of computer-assisted prognostication by discriminant analysis and the Bayesian classification procedure based on clinical information collected on patients with acute myocardial infarction. The overall accuracy was 94.2% in predicting hospital death but the prediction of late death after discharge was less accurate. It was found that not all of the 44 variables used for analysis were necessary to reach the same level of predictive accuracy--16 to 20 variables would result in almost the identical prediction. The Bayesian classification procedure was applied to estimate probabilities of individual patients belonging to the different prognostic categories.
D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis*; D011379:Prognosis; D012189:Retrospective Studies; D013223:Statistics as Topic