Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age

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.

Interventricular Septal Thickness as a Diagnostic Marker of Fetal Macrosomia

“…Prenatal ultrasound imaging was another subject of research. Ye et al aimed to investigate whether using ensemble methods on ultrasound measurements could improve the prediction of fetal macrosomia [ 17 ]. They concluded that ensemble learning, especially voting and stacking, can improve the prediction of fetal macrosomia and, as such, has the potential to assist obstetricians in making clinical decisions [ 17 ].…”

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

“…The most objective method currently employed to estimate fetal body weight is ultrasonographic (US) measurement, which encompasses over 30 different formulas for the US estimates to predict newborn birth weight [16][17][18], with the most widely used being the Hadlock formula [19]. To generate sonographic fetal weight estimations with a lower error margin, many formulas have reflected disparate parameters of the fetus (fetal abdominal fat layer [20], shoulder soft-tissue thickness [21], biacromial diameter [22]), and some have even entailed 3D sonographic measurements [23].…”

“…Shigemi et al created a scoring system based upon the significant predictors of macrosomia without sonographic information [4], and their system exhibited a high negative predictive value of 0.996-1.000, while the positive predictive value for screening macrosomia was extremely low (0.003). Zou et al [27] and Kang et al [28] published models that could only be applied to women with GDM rather than to all pregnant women, and Ye et al used ensemble methods (one comprising a machinelearning algorithm) to improve the prediction of fetal macrosomia [18]. Unfortunately, ensemble methods are cumbersome and limited in their practicability.…”

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

“…This study explored the key variables of depressive disorders in female older adults living alone using the stacking ensemble technique. A number of studies (14,15,22) have reported that the stacking ensemble model shows excellent accuracy because it compensates for the overfitting possibility, a disadvantage of a single predictive model. In other words, the goal of the stacking ensemble is to improve generalization capacity, and it has been widely used for classifying and developing predictive models using machine learning.…”