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Lian C, Wang Y, Bao X, Yang L, Liu G, Hao D, Zhang S, Yang Y, Li X, Meng Y, Zhang X, Li Z. Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm. Front Surg 2022; 9:951908. [PMID: 36211283 PMCID: PMC9538942 DOI: 10.3389/fsurg.2022.951908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/08/2022] [Indexed: 12/01/2022] Open
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.
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Affiliation(s)
- Cuiting Lian
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Yan Wang
- Department of Obstetrics, Peking University People’s Hospital, Beijing, China
| | - Xinyu Bao
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Lin Yang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
- Correspondence: Lin Yang Guoli Liu
| | - Guoli Liu
- Department of Obstetrics, Peking University People’s Hospital, Beijing, China
- Correspondence: Lin Yang Guoli Liu
| | - Dongmei Hao
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Song Zhang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Yimin Yang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Xuwen Li
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Yu Meng
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Xinyu Zhang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
| | - Ziwei Li
- Faculty of Environment and Life Sciences, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, China
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Vargas-Terrones M, Nagpal TS, Barakat R. Impact of exercise during pregnancy on gestational weight gain and birth weight: an overview. Braz J Phys Ther 2019; 23:164-169. [PMID: 30527949 PMCID: PMC6428912 DOI: 10.1016/j.bjpt.2018.11.012] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 11/05/2018] [Accepted: 11/08/2018] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE This article presents the state of knowledge related to the impact of exercise on gestational weight gain and birth weight. TRANSCENDENCE OF BABY WEIGHT Birth weight is an important indicator of intrauterine environment and maternal and newborn health. There are several factors that can affect birth weight including mother's pre-pregnancy Body Mass Index (BMI), gestational weight gain, Gestational Diabetes Mellitus (GDM), chronic diabetes and gestational age at birth. IMPACT OF EXERCISE DURING PREGNANCY Physical exercise has the potential to prevent excessive gestational weight gain, GDM and the potential complications associated with obesity during pregnancy. Therefore, women who regularly exercise during pregnancy are more likely to have an appropriate gestational weight gain and in turn, an appropriate birth weight infant, preventing being LGA without increasing risk of SGA, and this reduces risk factors for later life chronic disease development in the child including cardiovascular disease, obesity and diabetes. RECOMMENDATIONS It would be advisable to promote compliance with physical activity and exercise recommendations during pregnancy by using the specific resources to prescribe exercise to pregnant women without obstetric contraindications.
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Affiliation(s)
- Marina Vargas-Terrones
- AFIPE Research Group, Faculty of Sciences for Physical Activity and Sport, INEF, Universidad Politécnica de Madrid (UPM), Madrid, Spain.
| | - Taniya S Nagpal
- R. Samuel McLaughlin Foundation-Exercise and Pregnancy Laboratory, School of Kinesiology, The University of Western Ontario, London, Ontario, Canada
| | - Ruben Barakat
- AFIPE Research Group, Faculty of Sciences for Physical Activity and Sport, INEF, Universidad Politécnica de Madrid (UPM), Madrid, Spain
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