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

This study aims to predict fetal weight at varying gestational age in the absence of ultrasound examination within a certain accuracy. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4,212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersection-over-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show the loU between predicted range of fetal weight at any gestational age from the ensemble model and that from ultrasound. The machine learning based approach applied in our study is able to predict, with a high accuracy, fetal weight at varying gestational age in the absence of ultrasound examination.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research

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