Risk prediction of small for gestational age birth based on machine learning algorithms

ZHANG Ruimin; WANG Keke; LI Jinbo; CHEN Zhuanzhuan; YANG Hailan; WU Weiwei; FENG Yongliang; WANG Suping; ZHANG Xinri

doi:10.16462/j.cnki.zhjbkz.2023.08.009

Volume 27 Issue 8

Aug. 2023

Turn off MathJax

Article Contents

Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(8): 922-927

ZHANG Ruimin, WANG Keke, LI Jinbo, CHEN Zhuanzhuan, YANG Hailan, WU Weiwei, FENG Yongliang, WANG Suping, ZHANG Xinri. Risk prediction of small for gestational age birth based on machine learning algorithms[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 922-927. doi: 10.16462/j.cnki.zhjbkz.2023.08.009

Citation:

ZHANG Ruimin, WANG Keke, LI Jinbo, CHEN Zhuanzhuan, YANG Hailan, WU Weiwei, FENG Yongliang, WANG Suping, ZHANG Xinri. Risk prediction of small for gestational age birth based on machine learning algorithms[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 922-927. doi: 10.16462/j.cnki.zhjbkz.2023.08.009

Citation:

ZHANG Ruimin, WANG Keke, LI Jinbo, CHEN Zhuanzhuan, YANG Hailan, WU Weiwei, FENG Yongliang, WANG Suping, ZHANG Xinri. Risk prediction of small for gestational age birth based on machine learning algorithms[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 922-927. doi: 10.16462/j.cnki.zhjbkz.2023.08.009

PDF( 1412 KB)

Risk prediction of small for gestational age birth based on machine learning algorithms

doi: 10.16462/j.cnki.zhjbkz.2023.08.009

1.
Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2.
Health Management Center, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
3.
Obstetrics and Gynecology, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
4.
Pulmonary and Critical Care Medicine, the First Hospital of Shanxi Medical University, Taiyuan 030001, China

Funds:

Youth Scientific Research Project of Fundamental Research Program in Shanxi Province 20210302124581

More Information

Corresponding author: ZHANG Xinri, E-mail: ykdzxr61@163.com
Received Date: 2022-12-16
Rev Recd Date: 2023-01-26

Available Online: 2023-09-02

Publish Date: 2023-08-10

Abstract

Abstract

Objective To evaluate the performance of risk prediction of five machine learning models and traditional logistic regression models, such as, extreme gradient boosting (XGBoost), support vector machine (SVM), and Naive Bayes, aimed at small for gestational age (SGA). Methods A total of 9 972 women who gave birth in the First Hospital of Shanxi Medical University from March 2012 to September 2016 were selected as the research subjects in this study. Their data was collected from the hospital information system and through questionnaire surveys. Based on delivery outcomes, each case was put into one of two categories: an SGA group (n=1 124) and a non-SGA group (n=8 848), with the trial set and test set according to the ratio of 7.50∶2.50. Multivariate logistic regression model were used to screen the influencing factors. To establish predictive models, XGBoost, SVM, Naive Bayes, gradient boosting decision tree (GBDT) and k-nearest neighbor (KNN) algorithms were used. Furthermore, their predictive performance was measured with metrics such as the area under the curve (AUC), accuracy, and precision. Results Logistic regression analysis showed that gestational hypertension and eclampsia were among the seven variables related to the occurrence of SGA. By incorporating such variables into the machine learning algorithms and traditional logistic regression, the SVM model achieved the best performance with the highest AUC of 0.72 and 71% accuracy. Comparatively, compared to the SVM model, the logistic regression-based model was under performing, with an AUC of 0.71 and 66% accuracy. Conclusions Machine learning models, especially SVM, are capable of more accurately evaluating the risk of the occurrence of SGA in Shanxi Province, and can provide a reference for the primary prevention of SGA.
- Small for gestational age,
- Machine learning,
- Risk prediction model,
- Logistic regression model

FullText(HTML)

References(20)

References

[1]	Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee[J]. World Health Organ Tech Rep Ser, 1995, 854: 1-452.
[2]	Lee AC, Katz J, Blencowe H, et al. National and regional estimates of term and preterm babies born small for gestational age in 138 low-income and middle-income countries in 2010[J]. Lancet Glob Health, 2013, 1(1): e26-e36. DOI: 10.1016/S2214-109X(13)70006-8.
[3]	沈忠周, 王雅文, 马帅, 等. 新生儿早产、低出生体重及小于胎龄的危险因素[J]. 中华流行病学杂志, 2019, 40(9): 1125-1129. DOI: 10.3760/cma.j.issn.0254-6450.2019.09.020. Shen ZZ, Wang YW, Ma S, et al. Risk factors for preterm birth, low birth weight and small for gestational age: a prospective cohort study[J]. Chin J Epidemiol, 2019, 40(9): 1125-1129. DOI: 10.3760/cma.j.issn.0254-6450.2019.09.020.
[4]	von Beckerath AK, Kollmann M, Rotky-Fast C, et al. Perinatal complications and long-term neurodevelopmental outcome of infants with intrauterine growth restriction[J]. Am J Obstet Gynecol, 2013, 208(2): 130. e1-130. e6. DOI: 10.1016/j.ajog.2012.11.014.
[5]	Eves R, Mendonça M, Bartmann P, et al. Small for gestational age-cognitive performance from infancy to adulthood: an observational study[J]. BJOG, 2020, 127(13): 1598-1606. DOI: 10.1111/1471-0528.16341.
[6]	Lindqvist PG, Molin J. Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome?[J]. Ultrasound Obstet Gynecol, 2005, 25(3): 258-264. DOI: 10.1002/uog.1806.
[7]	D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets[J]. Lancet, 2021, 397(10270): 199-207. DOI: 10.1016/S0140-6736(20)32519-8.
[8]	欧阳平, 李小溪, 冷芬, 等. 机器学习算法在体检人群糖尿病风险预测中的应用[J]. 中华疾病控制杂志, 2021, 25(7): 849-853, 868. DOI: 10.16462/j.cnki.zhjbkz.2021.07.020. Ouyang P, Li XX, Leng F, et al. Application of machine learning algorithm in diabetes risk prediction of physical examination population[J]. Chin J Dis Control Prev, 2021, 25(7): 849-853, 868. DOI: 10.16462/j.cnki.zhjbkz.2021.07.020.
[9]	朱丽, 张蓉, 张淑莲, 等. 中国不同胎龄新生儿出生体重曲线研制[J]. 中华儿科杂志, 2015, 53(2): 97-103. DOI: 10.3760/cma.j.issn.0578-1310.2015.02.007. Zhu L, Zhang R, Zhang SL, et al. Chinese neonatal birth weight curve for different gestational age[J]. Chin J Pediatr, 2015, 53(2): 97-103. DOI: 10.3760/cma.j.issn.0578-1310.2015.02.007.
[10]	中国肥胖问题工作组数据汇总分析协作组. 我国成人体重指数和腰围对相关疾病危险因素异常的预测价值: 适宜体重指数和腰围切点的研究[J]. 中华流行病学杂志, 2002, 23(1): 5-10. DOI: 10.3760/j.issn:0254-6450.2002.01.003. Coorperative Meta-analysis Group of China Obesity Task Force. Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population[J]. Chin J Epidemiol, 2002, 23(1): 5-10. DOI: 10.3760/j.issn:0254-6450.2002.01.003.
[11]	Li Y, Guo H, Xiao L, et al. Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data[J]. Knowledge-Based Systems, 2016, 94: 88-104. DOI: 10.1016/j.knosys.2015.11.013.
[12]	Royal College of Obstetricians and Gynaecologists. The investigation and management of the small for gestational age fetus. Green-top Guideline No. 31[EB/OL]. (2013-03-22)[2023-02-16]. https://www.rcog.org.uk/en/guidelines-research-services/guidelines/gtg31/.
[13]	Papastefanou I, Wright D, Lolos M, et al. Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics, serum pregnancy-associated plasma protein-A and placental growth factor at 11-13 weeks' gestation[J]. Ultrasound Obstet Gynecol, 2021, 57(3): 392-400. DOI: 10.1002/uog.23118.
[14]	Gürgen F, Zengin Z, Varol F. Intrauterine growth restriction (IUGR) risk decision based on support vector machines[J]. Expert Syst Appl, 2012, 39(3): 2872-2876. DOI: 10.1016/j.eswa.2011.08.147.
[15]	Gardosi J, Madurasinghe V, Williams M, et al. Maternal and fetal risk factors for stillbirth: population based study[J]. BMJ, 2013, 346: f108. DOI: 10.1136/bmj.f108.
[16]	Gurung S, Tong HH, Bryce E, et al. A systematic review on estimating population attributable fraction for risk factors for small-for-gestational-age births in 81 low-and middle-income countries[J]. J Glob Health, 2022, 12: 04024. DOI: 10.7189/jogh.12.04024.
[17]	Parihar S, Singh S. Perinatal outcomes and intrahepatic cholestasis of pregnancy: a prospective study[J]. Int J Reprod Contracept Obstet Gynecol, 2019, 8(3): 1177-1182. DOI: 10.18203/2320-1770.ijrcog20190901.
[18]	Natarajan V, Singh P, Vigneshwar NKV, et al. Maternal and placental risk factors for small gestational age and fetal malnutrition[J]. Curr Pediatr Rev, 2023, 19(2): 187-196. DOI: 10.2174/1573396318666220705154424.
[19]	Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review[J]. J Biomed Inform, 2002, 35(5-6): 352-359. DOI: 10.1016/S1532-0464(03)00034-0.
[20]	Vapnik VN, Kotz S. Estimation of dependences based on empirical data[M]. New York: Springer Science & Business Media, 2006: 232-457.