Prediction methods for babies’ birth weight using linear and nonlinear regression analysis

Abstract

The aim of this study is to determine more accurate prediction methods between linear and non-linear methods for prediction of babies' birth weight among maternal demographic characteristics. Three hundred pregnant women were included in the study. Blood glucose level before and after ingestion of glucose load, age, body mass index, % of change in weight during pregnancy, height, gestational age, parity, and fetal sex were collected as independent variables and baby birth weight as dependent variable. In linear regression, least squares estimation method was used to estimate parameters. Non-linear regression method was performed using neural network model with multilayer perceptrons, back propagation method was preferred as learning algorithm. Coefficient of determination, R2, of the linear regression equation was found 59.8% and the standard error of the estimate was calculated as 325.69 gr. In non-linear regression method R2 value was also found 59.8% and standard error of estimate was calculated as 320.30 gr. According to the results of the present study, one method is not significantly better than the other. When "accuracy in prediction" is aimed, it is better to use the two methods and compare the results, and then decide on the selection of the favourable method.