Abstract
Background
Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean deliver, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories (below, within, or above recommended guidelines)
Methods
We analyzed data from the Araraquara Cohort, Brazil comprising 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthews correlation coefficient (MCC).
Results
The outcome were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The LightGBM model presented the best overall performance with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.756 for GWG below recommendations, and 0.624 for GWG above recommendations. The Random Forest model also performed well, achieving an AUC-ROC of 0.774 for GWG within recommendations, 0.732 for GWG below recommendations, and 0.593 for GWG above recommendations. The most importante were predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference.
Conclusion
Machine learning models can effectively predict GWG categories, providing a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes.