[Using principal component analysis to increase accuracy of prediction of metabolic syndrome in artificial neural network and logistic regression models]

ABSTRACT

Background and aims: In modeling process, correlation between covariates causes multicolinearity that may reduce efficiency of the model. This study was aimed to use principal component analysis to eliminate the effect of multicolinearity in logistic regression and neural network models, and to determine its effect on the accuracy of predicting metabolic syndrome in a sample of individuals participating in the Tehran Lipid and Glucose Study