Abstract
Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.
Highlights
Excessive fetal growth poses risks to maternal and infant well-being [1]
We investigate the use of ensemble methods to aggregate prediction results given by different estimated fetal weights (EFWs) empirical formulas and the statistical models
We proposed using ensemble methods to combine the strengths from nonlinear and quadratic mixed-effects models and 26 empirical formulas of EFWs to predict macrosomia and large for gestational age” (LGA) of newborns from sonographic ultrasound measurements
Summary
Excessive fetal growth poses risks to maternal and infant well-being [1]. The term “macrosomia” is used to describe the condition of a fetus with a birth weight of more than 4000 g, regardless of gestational age [1,2]. Zhang et al [4] took the empirical formula given by Hadlock et al [5] for estimating fetal weights and implemented a joint mixed-effects model to predict macrosomia and LGA. This procedure of predicting macrosomia or LGA from prenatal ultrasound measurements was a two-step supervised learning process. The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions
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