Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data
This study establishes a robust pipeline that significantly enhances the predictive performance of models for preeclampsia within imbalanced datasets. Our findings underscore the importance of a strategic approach to variable optimization in medical diagnostics, offering potential for broad application in various medical contexts where class imbalance is a concern.
Authors
- Hanlin Lv (ORCID: https://orcid.org/0000-0002-1876-7846)
- Yanhua Ma (ORCID: https://orcid.org/0000-0002-2254-3896)
- Yinyao Ma
- Longting Lv (ORCID: https://orcid.org/0009-0008-6009-4922)
- Lei Wang (ORCID: https://orcid.org/0009-0005-2757-1748)
- Xuxia Liang
- Xiao Wang (ORCID: https://orcid.org/0000-0002-0008-0659)
Institutions
- BGI Group (China) (CN)
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- BGI Research (CN)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1186/s13040-025-00440-1
- Citations
- 2
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 4.11
Funders
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China
- Guangxi Key Research and Development Program