Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor.
Fetal heart rate
In vitro fertilization-embryo transfer
Machine learning
Random forest
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
02
08
2019
accepted:
19
06
2020
pubmed:
6
7
2020
medline:
15
5
2021
entrez:
6
7
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND AND OBJECTIVE
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 32623348
pii: S0169-2607(20)31457-7
doi: 10.1016/j.cmpb.2020.105624
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105624Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.