Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer
Research questionCan machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?DesignA dual-center retrospective analysis of 1,664 SVBT cycles, including 308 early miscarriage cases, was conducted across two reproductive cen...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1557667/full |
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| author | Lidan Liu Bo Liu Huimei Wu Qiuying Gan Qianyi Huang Mujun Li |
| author_facet | Lidan Liu Bo Liu Huimei Wu Qiuying Gan Qianyi Huang Mujun Li |
| author_sort | Lidan Liu |
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| description | Research questionCan machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?DesignA dual-center retrospective analysis of 1,664 SVBT cycles, including 308 early miscarriage cases, was conducted across two reproductive centers. Multiple machine learning models, such as Logistic Regression, Random Forest, Gradient Boosting, and Voting Classifier, were developed. Metrics including Area Under the Curve(AUC), accuracy, precision, recall, F1 score, and specificity were used to evaluate model performance. Key predictors were identified through Mutual Information and Recursive Feature Elimination (RFE).ResultsMaternal age, paternal age, endometrial thickness, blastocyst quality, and ovarian stimulation parameters were identified as critical predictors. Compared to traditional statistical models such as logistic regression (AUC = 0.584), ensemble models demonstrated significantly improved predictive performance. The Voting Classifier achieved the highest AUC (0.836), accuracy (0.780), precision (0.914), and specificity (0.942), outperforming individual machine learning classifiers. The Gradient Boosting Classifier also exhibited strong performance (AUC 0.831, accuracy 0.777), confirming the effectiveness of ensemble learning in capturing complex predictors of early miscarriage risk.ConclusionEnsemble machine learning models, particularly the Voting Classifier and Gradient Boosting Classifier, significantly improve the prediction of early miscarriage following SVBT. These models provide accurate, individualized risk assessments, enhancing clinical decision-making and advancing personalized care in ART. |
| format | Article |
| id | doaj-art-0124bb6c951e491b9a6078f266c1a659 |
| institution | OA Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj-art-0124bb6c951e491b9a6078f266c1a6592025-08-20T02:26:22ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-04-011610.3389/fendo.2025.15576671557667Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transferLidan Liu0Bo Liu1Huimei Wu2Qiuying Gan3Qianyi Huang4Mujun Li5Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaGuangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaGuangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaReproductive Center, Nanning Maternity and Child Health Hospital, Nanning, Guangxi, ChinaGuangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaGuangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaResearch questionCan machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?DesignA dual-center retrospective analysis of 1,664 SVBT cycles, including 308 early miscarriage cases, was conducted across two reproductive centers. Multiple machine learning models, such as Logistic Regression, Random Forest, Gradient Boosting, and Voting Classifier, were developed. Metrics including Area Under the Curve(AUC), accuracy, precision, recall, F1 score, and specificity were used to evaluate model performance. Key predictors were identified through Mutual Information and Recursive Feature Elimination (RFE).ResultsMaternal age, paternal age, endometrial thickness, blastocyst quality, and ovarian stimulation parameters were identified as critical predictors. Compared to traditional statistical models such as logistic regression (AUC = 0.584), ensemble models demonstrated significantly improved predictive performance. The Voting Classifier achieved the highest AUC (0.836), accuracy (0.780), precision (0.914), and specificity (0.942), outperforming individual machine learning classifiers. The Gradient Boosting Classifier also exhibited strong performance (AUC 0.831, accuracy 0.777), confirming the effectiveness of ensemble learning in capturing complex predictors of early miscarriage risk.ConclusionEnsemble machine learning models, particularly the Voting Classifier and Gradient Boosting Classifier, significantly improve the prediction of early miscarriage following SVBT. These models provide accurate, individualized risk assessments, enhancing clinical decision-making and advancing personalized care in ART.https://www.frontiersin.org/articles/10.3389/fendo.2025.1557667/fullearly miscarriagesingle vitrified-warmed blastocyst transfer (SVBT)machine learning (ML)voting classifiergradient boosting |
| spellingShingle | Lidan Liu Bo Liu Huimei Wu Qiuying Gan Qianyi Huang Mujun Li Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer Frontiers in Endocrinology early miscarriage single vitrified-warmed blastocyst transfer (SVBT) machine learning (ML) voting classifier gradient boosting |
| title | Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer |
| title_full | Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer |
| title_fullStr | Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer |
| title_full_unstemmed | Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer |
| title_short | Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer |
| title_sort | optimizing predictive features using machine learning for early miscarriage risk following single vitrified warmed blastocyst transfer |
| topic | early miscarriage single vitrified-warmed blastocyst transfer (SVBT) machine learning (ML) voting classifier gradient boosting |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1557667/full |
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