Lijue Liu1, Yongxia Jiao2, Xihong Li3, Yan Ouyang4, Danni Shi2. 1. School of Automation, Central South University, Changsha, Hunan, 410083, China; Hunan Zixing Intelligent Medical Technology Co., Ltd, Changsha, Hunan, 410000, China. 2. School of Automation, Central South University, Changsha, Hunan, 410083, China. 3. Reproductive and Genetic Hospital of CITIC-Xiangya, No. 84, Xiangya road, Changsha city, Hunan, 410078, China. Electronic address: 2575031980@qq.com. 4. Reproductive and Genetic Hospital of CITIC-Xiangya, No. 84, Xiangya road, Changsha city, Hunan, 410078, China; Institute of Reproductive and Stem Cell Engineering, Central South University, No. 84, Xiangya road, Changsha city, Hunan, 410078, China.
Abstract
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.
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.