[The comparison of logistic regression model selection methods for the prediction of coronary artery disease].
OBJECTIVE
In this study, logistic regression model selection methods were compared for the prediction of coronary artery disease (CAD).


METHODS
Coronary artery disease data were taken from 237 consecutive people who had been applied to Inönü University Faculty of Medicine, Department of Cardiology. Logistic regression model selection methods were applied to CAD data containing continuous and discrete independent variables. Goodness of fit test was performed by Hosmer-Lemeshow statistic. Likelihood-ratio statistic was used to compare the estimated models.


RESULTS
Each of the logistic regression model selection methods had sensitivity, specificity and accuracy rates greater than 91.9%. Hosmer-Lemeshow statistic showed that the model selection methods were successful in the description of CAD data. Related factors with CAD were identified and the results were evaluated.


CONCLUSIONS
Logistic regression model selection methods were very successful in the prediction of CAD. Stepwise model selection methods were better than Enter method based on Likelihood-ratio statistic for the prediction of CAD. Age, diabetes mellitus, hypertension, family history, smoking, low-density lipoprotein, triglyceride, stress and obesity variables may be used for the prediction of CAD.
Colak
Cemil
C
Turkish Standards Institute, Ankara, Turkey. cemilcolak@yahoo.com
Colak
M Cengiz
MC
Orman
Mehmet N
MN
D016022:Case-Control Studies; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D014421:Turkey / Q000453:epidemiology