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This project explores the use of Machine Learning algorithms to enhance fetal weight estimation, outperforming traditional regression-based models. By leveraging ensemble learning techniques (XGBoost, Random Forest, LightGBM, CatBoost), the models significantly reduce prediction errors, making them more accurate and reliable for clinical practice.

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