AUTHOR=Lian Cuiting , Wang Yan , Bao Xinyu , Yang Lin , Liu Guoli , Hao Dongmei , Zhang Song , Yang Yimin , Li Xuwen , Meng Yu , Zhang Xinyu , Li Ziwei TITLE=Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm JOURNAL=Frontiers in Surgery VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.951908 DOI=10.3389/fsurg.2022.951908 ISSN=2296-875X ABSTRACT=Background

This study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR.

Objective

To establish a dynamic prediction model of FGR.

Methods

This study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks.

Results

At 20–24 weeks and 25–29 weeks of gestation, the effect of the multivariate Logistic method on model prediction was better. At 30–34 weeks of gestation, the prediction effect of FGR model using the SVM method is better. The ROC curve area was above 85%.

Conclusions

The dynamic prediction model of FGR based on SVM and logistic regression is helpful to improve the sensitivity of FGR in pregnant women during prenatal screening. The establishment of prediction models at different gestational ages can effectively predict whether the fetus has FGR, and significantly improve the clinical treatment effect.