[Using principal component analysis to increase accuracy of prediction of metabolic syndrome in artificial neural network and logistic regression models]
Journal of Shahrekord University of Medical Sciences. 2011; 13 (4): 18-27
en Fa
| IMEMR
| ID: emr-194655
Biblioteca responsable:
EMRO
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
Buscar en Google
Índice:
IMEMR
Idioma:
Fa
Revista:
J. Shahrekord Univ. Med. Sci.
Año:
2011