The comparison of logistic regression model selection methods for the prediction of coronary artery disease

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