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

Björnsson, Ola, Sara Alson
2025 · doi:10.5281/zenodo.17715463 · W7106668813
other OA: green CC0
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This study developed and evaluated machine learning models to predict live birth after IVF by analyzing ultrasound features of adenomyosis according to MUSA criteria.

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This software repository presents XGBoost-based machine learning models designed to predict live birth rates following in vitro fertilization treatment. The study incorporates specific ultrasound features of adenomyosis, utilizing the Morphological Uterus Sonographic Assessment criteria as key input variables for the prediction algorithm. Methodological components include hyperparameter optimization via Optuna, cross-validation procedures, and feature importance analysis using SHAP values to interpret model outcomes. This paper is centrally about adenomyosis — specifically, it uses adenomyosis ultrasound features to enhance predictive accuracy for IVF success rates.

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Abstract

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.
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Published November 25, 2025 | Version v1.0.0 Software Open ivf_lbr_prediction: Machine Learning Prediction of Live Birth After IVF Using Adenomyosis Features Authors/Creators - 1. Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden - 2. Obstetric, Gynecological and Prenatal Ultrasound research, Department of Clinical Sciences, Malmö, Lund University, Sweden Description 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. Files F-Ursus/ivf_lbr_prediction-v1.0.0.zip Files (32.9 kB) | Name | Size | Download all | |---|---|---| | md5:b5fe8ff6176de76c56086b58e05bc152 | 32.9 kB | Preview Download | Additional details Related works - Is supplement to - Software: https://github.com/F-Ursus/ivf_lbr_prediction/tree/v1.0.0 (URL) Software - Repository URL - https://github.com/F-Ursus/ivf_lbr_prediction

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Outcome instruments

MUSA

Condition tags

adenomyosis

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