Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer
The XGBoost model predicted the possibility of pregnancy with an accuracy of up to 0.999. While the LightGBM model predicted the possibility of live birth with an accuracy of up to 0.913.
Authors
- Xujun Xuan (ORCID: https://orcid.org/0009-0000-3493-1055)
- Ru Bai (ORCID: https://orcid.org/0000-0001-7312-7086)
- Xia Hong
- Ya Tuo (ORCID: https://orcid.org/0000-0002-7768-168X)
- Xiaohe Li (ORCID: https://orcid.org/0000-0003-4315-5953)
- Jiawei Li (ORCID: https://orcid.org/0000-0002-6253-1582)
Institutions
- Inner Mongolia University of Science and Technology (CN)
- Baotou Medical College (CN)
- Inner Mongolia Medical University (CN)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1186/s12884-025-07433-2
- Citations
- 9
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 16.36
Funders
- Natural Science Foundation of Inner Mongolia