Interpretable machine learning prediction of live birth after freeze-all FET cycles across transfer-order subgroups
BackgroundTransfer-order heterogeneity may affect live-birth prediction after freeze-all FET cycles, but existing prediction studies have rarely modeled first- and second-transfer records separately.MethodsWe developed and compared logistic regression (LR), support vector machine, random forest, XGB...
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| Principais autores: | , , , , , , , |
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| 格式: | Artigo |
| 語言: | Inglês |
| 出版: |
Frontiers Media S.A.
2026-07-01
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| 叢編: | Frontiers in Endocrinology |
| 主題: | |
| 在線閱讀: | https://www.frontiersin.org/articles/10.3389/fendo.2026.1868575/full |
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