Background and objective

According to previous studies, after in vitro fertilization-embryo transfer (IVF-ET) there exist a high early pregnancy loss (EPL) rate. The objectives of this study were to construct a prediction model of embryonic development by using machine learning algorithms based on historical case data, in this way doctors can make more accurate suggestions on the number of patient follow-ups, and provide decision support for doctors who are relatively inexperienced in clinical practice.

Methods

We analyzed the significance of the same type of features between ongoing pregnancy samples and EPL samples. At the same time, by analyzing the correlation between days after embryo transfer (ETD) and fetal heart rate (FHR) of those normal embryo samples, a regression model between the two was established to obtain FHR model of normal development, and the residual analysis was used to further clarify the importance of FHR in predicting pregnancy outcome. Finally we applied six representative machine learning algorithms including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Back Propagation Neural Network (BNN), XGBoost and Random Forest (RF) to build prediction models. Sensitivity was selected to evaluate prediction results, and accuracy of what each algorithm above predicted under both the conditions with and without FHR was compared as well.

Results

There were statically significant differences in the same type of features between ongoing pregnancy samples and EPL samples, which could serve as predictors. FHR, of which the normal development showed a strong correlation with ETD, had great predictive value for embryonic development. Among the six predictive models the one predicted with the highest accuracy was Random Forest, of which recall ratio and F1 could reach 97%, and AUC could reach 0.97, FHR taken into account as a feature. In addition, Random Forest had a higher prediction accuracy rate for samples with longer ETD—its accuracy rate could reach 99% when predicting those at 10 weeks after embryo transfer.

Conclusion

In this study, we established and compared six classification models to accurately predict EPL after the appearance of embryonic cardiac activity undergoing IVF-ET. Finally, Random Forest model outperformed the others. The implementation of Random Forest model in clinical environment can assist doctors to make clinical decisions.

中文翻译：

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机器学习算法可预测体外受精-胚胎移植后的早期妊娠损失，其中胎心率是一个强有力的预测指标。

背景和目标

根据以前的研究，体外受精-胚胎移植（IVF-ET）后，早期妊娠流失率（EPL）很高。这项研究的目的是通过使用基于历史病例数据的机器学习算法来构建胚胎发育的预测模型，以这种方式，医生可以对患者的随访次数提出更准确的建议，并为那些需要随访的医生提供决策支持在临床实践中相对缺乏经验。

方法

我们分析了正在进行的妊娠样本和EPL样本之间相同类型特征的重要性。同时，通过分析正常胚胎样本的胚胎移植后天数（ETD）与胎儿心率（FHR）之间的相关性，建立两者之间的回归模型，以获得正常发育的FHR模型，并进行残差分析。用于进一步阐明FHR在预测妊娠结局中的重要性。最后，我们应用了六种代表性的机器学习算法，包括Logistic回归（LR），支持向量机（SVM），决策树（DT），反向传播神经网络（BNN），XGBoost和随机森林（RF）来构建预测模型。选择敏感性来评估预测结果，

结果

进行中的妊娠样本和EPL样本之间，同一类型的特征在静态上存在显着差异，可以用作预测指标。正常发育与ETD密切相关的FHR对胚胎发育具有很大的预测价值。在这六种预测模型中，预测精度最高的是随机森林，其召回率和F1可以达到97％，AUC可以达到0.97，其中以FHR为特征。此外，随机森林对于ETD较长的样本具有较高的预测准确率-当预测胚胎移植后10周的样本时，其准确率可以达到99％。

结论

在这项研究中，我们建立并比较了六个分类模型，以准确预测经过IVF-ET的胚胎心脏活动出现后的EPL。最后，随机森林模型的表现优于其他模型。在临床环境中实施随机森林模型可以帮助医生做出临床决策。