Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning

Highlights：

• Develop a robust methodology to predict fetal weight at varying gestational age in the absence of ultrasound examination.

• Establish a dataset consisting of 4212 clinical records based on the electronic health record of pregnant women from a large hospital in China.

• Establish a temporal relationship between the gestational age and the main characteristics of fetal growth on Chinese population.

• Construct an ensemble learning model based on three machine learning algorithms, which is optimised in parallel via a multi-parameter genetic algorithm.

• Prove to be an accurate estimation tool for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring.