Construction of machine learning tools to predict threatened miscarriage in the first trimester based on AEA, progesterone and β-hCG in China: a multicentre, observational, case-control study

Abstract

Background Endocannabinoid anandamide (AEA), progesterone (P4) and β-human chorionic gonadotrophin (β-hCG) are associated with the threatened miscarriage in the early stage. However, no study has investigated whether combing these three hormones could predict threatened miscarriage. Thus, we aim to establish machine learning models utilizing these three hormones to predict threatened miscarriage risk. Methods This is a multicentre, observational, case-control study involving 215 pregnant women. We recruited 119 normal pregnant women and 96 threatened miscarriage pregnant women including 58 women with ongoing pregnancy and 38 women with inevitable miscarriage. P4 and β-hCG levels were detected by chemiluminescence immunoassay assay. The level of AEA was tested by ultra-high-performance liquid chromatography-tandem mass spectrometry. Six predictive machine learning models were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), accuracy and precision. Results The median concentration of AEA was significantly lower in the healthy pregnant women group than that in the threatened miscarriage group, while the median concentration of P4 was significantly higher in the normal pregnancy group than that in the threatened miscarriage group. Only the median level of P4 was significantly lower in the inevitable miscarriage group than that in the ongoing pregnancy group. Moreover, AEA is strongly positively correlated with threatened miscarriage, while P4 is negatively correlated with both threatened miscarriage and inevitable miscarriage. Interestingly, AEA and P4 are negatively correlated with each other. Among six models, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) models obtained the AUC values of 0.75, 0.70 and 0.70, respectively; and their accuracy and precision were all above 0.60. Among these three models, the LR model showed the highest accuracy (0.65) and precision (0.70) to predict threatened miscarriage. Conclusions The LR model showed the highest overall predictive power, thus machine learning combined with the level of AEA, P4 and β-hCG might be a new approach to predict the threatened miscarriage risk in the near feature.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

“…In studies about the prediction of miscarriage risk in women with immunologically abnormal pregnancies, various methods have been employed to investigate high-risk factors in pregnant women. However, there are still shortcomings in clinical practice, model performance evaluation, and the practical application of prediction tools, including application complexity ( Bruno et al, 2020 ; Benner et al, 2022 ; Huang et al, 2022 ; Macrohon et al, 2022 ; Hao et al, 2023 ; Luo and Zhou, 2023 ). Additionally, there is a lack of studies conducting retrospective or prospective validation of pregnancy risk prediction models and evaluating the economic aspects of these models.…”

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

“…Review of Related Works The computational learning approach has been proven to be a reliable tool in predicting various maternal outcomes, such as full-term delivery (Predicting induced labour outcomes for full-term pregnancies using an Intuitionistic Fuzzy Approach for maternal outcome prediction [12], [13]), miscarriage (proposed early prediction for both miscarriage and threatened miscarriage [3], [14], [15]), mortality (implemented machine learning techniques to forecast in-hospital mortality [16]- [18]),placenta previa, preterm delivery (utilizing machine learning for the early prediction of spontaneous preterm birth [19]- [22]), stillbirth (Data-Driven Stillbirth Prediction in Pregnancy [23]), and Urinary Tract Infection (UTI) exploring machine learning algorithms for predicting UTI [24]- [27]. It is encouraging to see the advancements in this field, which have the potential to improve the health and safety of expectant mothers.…”