ivf_lbr_prediction: Machine Learning Prediction of Live Birth After IVF Using Adenomyosis Features

XGBoost-based machine learning models for predicting live birth rates following in vitro fertilization (IVF) treatment, incorporating ultrasound features of adenomyosis based on the Morphological Uterus Sonographic Assessment (MUSA) criteria. This repository contains code for hyperparameter optimization using Optuna, cross-validation, model evaluation, and feature importance analysis using SHAP values.