[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120909-en":3,"doc-seo-120909-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120909,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms","Developing and validating machine learning models to predict preterm birth (\u003C37 weeks’ gestation) in singleton pregnancies at different gestational intervals using routine surveillance data. Models were trained on complete data from 22,603 singleton pregnancies collected in 51 midwifery clinics and hospitals in Wenzhou, China (2014–2016). CatBoost, Random Forest, stacked models, deep neural networks, SVM, and logistic regression were used for permutation-based feature selection and 5-fold cross-validation. The CatBoost model after 26 weeks’ gestation achieved AUC 0.70 (0.67–0.73), accuracy 0.81, sensitivity 0.47, and specificity 0.83.","TYPE Original Research PUBLISHED 29 February 2024 DOI 10. 3389/fdata.2024.1291196  \nOPEN ACCESS  \nEDITED BY  \nLin-Ching Chang,  \nThe Catholic University of America, United States  \nREVIEWED BY  \nTalayeh Razzaghi,  \nUniversity of Oklahoma, United States Silvia Filogna,  \nStella Maris Foundation (IRCCS), Italy  \n*CORRESPONDENCE  \nJoris Hemelaar  \n [joris.hemelaar@npeu.ox.ac.uk](joris.hemelaar@npeu.ox.ac.uk)[ ](joris.hemelaar@npeu.ox.ac.uk)[Xin-Jun Yang](Xin-Jun Yang)  \n [xjyang@wmu.edu.cn](xjyang@wmu.edu.cn)  \nRECEIVED 08 September 2023  \nACCEPTED 12 February 2024  \nPUBLISHED 29 February 2024  \nCITATION  \nYu Q-Y, Lin Y, Zhou Y-R, Yang X-J and Hemelaar J (2024) Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.  \nFront. Big Data 7:1291196 .  \ndoi: 10.3389/fdata.2024.1291196  \nCOPYRIGHT  \n© 2024 Yu, Lin, Zhou, Yang and Hemelaar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting risk of preterm birth in singleton pregnancies using machine learning algorithms  \nQiu-Yan Yu1,2 , Ying Lin3 , Yu-Run Zhou3 , Xin-Jun Yang2* and Joris Hemelaar1*  \n1 National Perinatal Epidemiology Unit, Nu􀀈eld 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  \nWe aimed to develop, train, and validate machine learning models for predicting preterm birth ( \u003C37 weeks’ gestation) in singleton pregnancies at di􀀀erent 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 permutationbased 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 CatBoostbased 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 weidentified several modifiable antenatal predictors.  \nKEYWORDS  \npreterm birth, machine learning, prediction models, antenatal care, feature selection  \nIntroduction  \nPreterm birth (PTB) is the leading cause of neonatal and child mortality globally (Liu et al., 2016) . United Nations Sustainable Development Goal 3 target 3.2 aims to reduce neonatal and child mortality to 12 per 1,000 live births and 25 per 1,000 live births, respectively (United Nations, 2016) . A recent study estimated that 10.6% of all babies worl","cbCailftyM5M7gWh","https://ap.wps.com/l/cbCailftyM5M7gWh","pdf",1908384,1,14,"English","en",105,"# Introduction\n## Burden and public health relevance of preterm birth\n## Limitations of current screening approaches\n## Rationale for machine learning with routine surveillance data\n## Prior work on machine learning prediction models","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To develop, train, and validate machine learning models for predicting preterm birth in singleton pregnancies at different gestational intervals.\"},{\"question\":\"Which algorithms and prediction framework were used?\",\"answer\":\"CatBoost, Random Forest, stacked models, deep neural networks, support vector machines, and logistic regression were used, along with permutation-based feature selection and 5-fold cross-validation.\"},{\"question\":\"How did the best-performing model perform and at which gestational time?\",\"answer\":\"The CatBoost-based model after 26 weeks’ gestation performed best, with AUC 0.70 (0.67–0.73), accuracy 0.81, sensitivity 0.47, and specificity 0.83.\"}]","Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms | 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was the main objective of the study?","Question",{"text":75,"@type":76},"To develop, train, and validate machine learning models for predicting preterm birth in singleton pregnancies at different gestational intervals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms and prediction framework were used?",{"text":80,"@type":76},"CatBoost, Random Forest, stacked models, deep neural networks, support vector machines, and logistic regression were used, along with permutation-based feature selection and 5-fold cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the best-performing model perform and at which gestational time?",{"text":84,"@type":76},"The CatBoost-based model after 26 weeks’ gestation performed best, with AUC 0.70 (0.67–0.73), accuracy 0.81, sensitivity 0.47, and specificity 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