Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

doi:10.3389/fdata.2024.1291196

科研成果详情

题名	Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms
作者	Yu, Qiu-Yan1,2; Lin, Ying 3; Zhou, Yu-Run 3; Yang, Xin-Jun2; Hemelaar, Joris 1
发表日期	2024-02-29
发表期刊	Frontiers in big data 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	antenatal care feature selection machine learning prediction models preterm birth
摘要	We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.
资助项目	Natural Science Foundation of Zhejiang Province10.13039/501100004731
出版者	FRONTIERS MEDIA SA
ISSN	2624-909X
EISSN	2624-909X
卷号	7
DOI	10.3389/fdata.2024.1291196
页数	14
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS研究方向	Computer Science ; Science & Technology - Other Topics
WOS记录号	WOS:001185062700001
收录类别	PUBMED ; SCOPUS ; ESCI
URL	查看原文
PubMed ID	38495848
SCOPUSEID	2-s2.0-85187866703
通讯作者地址	[Hemelaar, Joris]National Perinatal Epidemiology Unit,Nuffield Department of Population Health,University of Oxford,Oxford,United Kingdom ; [Yang, Xin-Jun]Department of Preventive Medicine,School of Public Health,Wenzhou Medical University,Wenzhou,China
Scopus学科分类	Computer Science (miscellaneous);Information Systems;Artificial Intelligence
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/210300
专题	公共卫生学院_预防医学系
通讯作者	Yang, Xin-Jun; Hemelaar, Joris
作者单位	1.National Perinatal Epidemiology Unit,Nuffield Department of Population Health,University of Oxford,Oxford,United Kingdom; 2.Department of Preventive Medicine,School of Public Health,Wenzhou Medical University,Wenzhou,China; 3.Wenzhou Women and Children Health Guidance Center,Wenzhou,China
第一作者单位	公共卫生学院_预防医学系
通讯作者单位	公共卫生学院_预防医学系
推荐引用方式 GB/T 7714	Yu, Qiu-Yan,Lin, Ying,Zhou, Yu-Run,et al. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms[J]. Frontiers in big data,2024,7.
APA	Yu, Qiu-Yan, Lin, Ying, Zhou, Yu-Run, Yang, Xin-Jun, & Hemelaar, Joris. (2024). Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Frontiers in big data, 7.
MLA	Yu, Qiu-Yan,et al."Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms".Frontiers in big data 7(2024).