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

共 50 条

[31] A New XGBoost Algorithm Based Prediction Model for Fetal Growth Restriction in Patients with Preeclampsia
Li, Haijuan
Wang, Sumei
Zhan, Chunyu
CLINICAL AND EXPERIMENTAL OBSTETRICS & GYNECOLOGY, 2023, 50 (08):
[32] Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction
Wei, Yan
Rao, Xili
Fu, Yinjun
Song, Li
Chen, Huiling
Li, Junhong
PLOS ONE, 2023, 18 (11):
[33] Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms
Lee, Seon Ui
Choi, Sae Kyung
Jo, Yun Sung
Wie, Jeong Ha
Shin, Jae Eun
Kim, Yeon Hee
Kil, Kicheol
Ko, Hyun Sun
LIFE-BASEL, 2024, 14 (11):
[34] PREDICTION OF LIVER RELATED TOXICITY EFFECTS USING LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINE TECHNIQUES
Gunturi, Sitarama
Ramamurthi, Narayanan
Theerthala, Sivarama Sarma
DRUG METABOLISM REVIEWS, 2012, 44 : 101 - 101
[35] Prediction of the rate of penetration using logistic regression algorithm of machine learning model
Deng S.
Wei M.
Xu M.
Cai W.
Arabian Journal of Geosciences, 2021, 14 (21)
[36] Seasonal prediction of PM2.5 based on support vector machine model and multiple regression model
Yang, Shuran
INTERNATIONAL CONFERENCE ON ALGORITHMS, HIGH PERFORMANCE COMPUTING, AND ARTIFICIAL INTELLIGENCE (AHPCAI 2021), 2021, 12156
[37] Online prediction model based on support vector machine
Wang, Wenjian
Men, Changqian
Lu, Weizhen
NEUROCOMPUTING, 2008, 71 (4-6) : 550 - 558
[38] Terrorism Risk Prediction Model Based on Support Vector Machine Optimized by Whale Algorithm
Luan, Meng
Sun, Duoyong
Li, Zhanfeng
PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019), 2019, : 166 - 169
[39] Prediction of Coal Calorific Value Based on a Hybrid Linear Regression and Support Vector Machine Model
Sun, K. (klsun@aust.edu.cn), 1600, Springer Verlag (212):
[40] Support vector machine regression algorithm based on chunking incremental learning
Jiang Jingqing
Song Chuyi
Wu Chunguo
Maurizio, Marchese
Liang Yangchun
COMPUTATIONAL SCIENCE - ICCS 2006, PT 1, PROCEEDINGS, 2006, 3991 : 547 - 554

← 1 2 3 4 5 →