Liu Lidan, Liu Bo, Wu Huimei, Gan Qiuying, Huang Qianyi, Li Mujun
Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Reproductive Center, Nanning Maternity and Child Health Hospital, Nanning, Guangxi, China.
Front Endocrinol (Lausanne). 2025 Apr 16;16:1557667. doi: 10.3389/fendo.2025.1557667. eCollection 2025.
Can machine learning models accurately predict the risk of early miscarriage following single vitrified-warmed blastocyst transfer (SVBT)?
A 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).
Maternal 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.
Ensemble 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.
机器学习模型能否准确预测单囊胚玻璃化冷冻-解冻移植（SVBT）后早期流产的风险？
对两个生殖中心的1664个SVBT周期进行双中心回顾性分析，其中包括308例早期流产病例。开发了多种机器学习模型，如逻辑回归、随机森林、梯度提升和投票分类器。使用曲线下面积（AUC）、准确率、精确率、召回率、F1分数和特异性等指标来评估模型性能。通过互信息和递归特征消除（RFE）确定关键预测因素。
产妇年龄、父亲年龄、子宫内膜厚度、囊胚质量和卵巢刺激参数被确定为关键预测因素。与逻辑回归等传统统计模型（AUC = 0.584）相比，集成模型的预测性能有显著提高。投票分类器的AUC（0.836）、准确率（0.780）、精确率（0.914）和特异性（0.942）最高，优于单个机器学习分类器。梯度提升分类器也表现出强大的性能（AUC 0.831，准确率0.777），证实了集成学习在捕捉早期流产风险复杂预测因素方面的有效性。
集成机器学习模型，特别是投票分类器和梯度提升分类器，显著提高了SVBT后早期流产的预测能力。这些模型提供了准确的个性化风险评估，增强了临床决策，并推动了辅助生殖技术中的个性化护理。
