Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm - Lian, Cuiting; Wang, Yan; Bao, Xinyu; Yang, Lin; Liu, Guoli; Hao, Dongmei; Zhang, Song; Yang, Yimin; Li, Xuwen; Meng, Yu; Zhang, Xinyu; Li, Ziwei (Frontiers in Surgery, 2022)
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 by gestational age. Methods: In this study, two risk factors and support vector machine (SVM) and multiple logistic regression algorithms were used to establish prediction models for FGR at different gestational weeks. Results: At 20-24 weeks of gestation and 25-29 weeks of gestation, the effect of multivariate Logistic method on model prediction was better. At 30 to 34 weeks of gestation, the prediction effect of FGR model using 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. Keywords: fetal growth restriction, FGR, dynamic prediction,prediction model, multiple gestational age