Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study

Objective: This study aimed to develop machine learning (ML)-based models for the early prediction of macrosomia using only maternal sociodemographic and obstetric data. Methods: This retrospective study included 100 pregnant women who delivered at the Obstetric Clinic of Eskisehir City Hospital between January 2022 and December 2023. Participants were classified as nulliparous (n = 48) or parous (n = 52) and further categorized according to the presence or absence of fetal macrosomia (birth weight > 4000 g). Predictor variables included maternal age, body mass index (BMI), gravida, smoking status, history of diabetes mellitus, and hypertension; previous birth weight was additionally included in the parous model. Separate machine learning models were developed for nulliparous and parous women using Extra Trees Classifier, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB) Classifier, Random Forest, and Logistic Regression. The dataset was randomly divided into training (80%) and testing (20%) subsets. Internal validation was performed using 10-fold cross-validation within the training dataset to optimize model performance and reduce overfitting. Given the relatively small sample size, this study was designed as a pilot exploratory investigation. Results: Overall, 48 nulliparous and 52 parous mothers were included in the study. Among the nulliparous women, 22 (45.8%) had macrosomic newborns, whereas 26 (54.2%) had normal birthweight newborns. Among parous women, 28 (53.8%) had macrosomic newborns, while 24 (46.2%) had normal birthweight newborns. For nulliparous mothers, the XGB Classifier achieved the highest accuracy (80%) and AUC-ROC (82.2%), demonstrating robust predictive performance. For parous mothers, the XGB Classifier again outperformed other ML models, achieving an accuracy of 72.7% and an AUC-ROC of 83.3% in predicting macrosomia. Conclusions: This study highlights the feasibility of ML-based decision support systems in obstetrics, particularly in low-resource settings, to predict macrosomia using readily available maternal characteristics. As a pilot study, these findings should be interpreted cautiously and require validation in larger multicenter cohorts before clinical implementation.