Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm

被引：3

作者：

Lian, Cuiting ^{[1
]}

Wang, Yan ^{[2
]}

Bao, Xinyu ^{[1
]}

Yang, Lin ^{[1
]}

Liu, Guoli ^{[2
]}

Hao, Dongmei ^{[1
]}

Zhang, Song ^{[1
]}

Yang, Yimin ^{[1
]}

Li, Xuwen ^{[1
]}

Meng, Yu ^{[1
]}

Zhang, Xinyu ^{[1
]}

Li, Ziwei ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Environm & Life Sci, Intelligent Physiol Measurement & Clin Translat, Beijing Int Base Sci & Technol Cooperat, Beijing, Peoples R China

[2] Peking Univ Peoples Hosp, Dept Obstet, Beijing, Peoples R China

来源：

FRONTIERS IN SURGERY | 2022年 / 9卷

基金：

中国国家自然科学基金;

关键词：

fetal growth restriction; FGR; dynamic prediction; prediction model; multiple gestational weeks; DISORDERS; PREGNANCY; PROTEIN; WEIGHT; HEIGHT;

D O I：

10.3389/fsurg.2022.951908

中图分类号：

R61 [外科手术学];

学科分类号：

摘要：

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.

引用

页数：8

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