NDLI: Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China

Content Provider	Springer Nature : BioMed Central
Author	Ding, Liwen Yin, Xiaona Wen, Guomin Sun, Dengli Xian, Danxia Zhao, Yafen Zhang, Maolin Yang, Weikang Chen, Weiqing
Abstract	Background Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive methods face challenges due to heterogeneous risk factors and their interaction effects. This study aims to develop and evaluate six machine learning (ML) models to predict PTB using large-scale children survey data from Shenzhen, China, and to identify key predictors through Shapley Additive Explanations (SHAP) analysis. Methods Data from 84,050 mother–child pairs, collected in 2021 and 2022, were processed and divided into training, validation, and test sets. Six ML models were tested: L1-Regularised Logistic Regression, Light Gradient Boosting Machine (LightGBM), Naive Bayes, Random Forests, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated based on discrimination, calibration and clinical utility. SHAP analysis was used to interpret the importance and impact of individual features on PTB prediction. Results The XGBoost model demonstrated the best overall performance, with the area under the receiver operating characteristic curve (AUC) scores of 0.752 and 0.757 in the validation and test sets, respectively, along with favorable calibration and clinical utility. Key predictors identified were multiple pregnancies, threatened abortion, and maternal age of conception. SHAP analysis highlighted the positive impacts of multiple pregnancies and threatened abortion, as well as the negative impact of micronutrient supplementation on PTB. Conclusion Our study found that ML models, particularly XGBoost, show promise in accurately predicting PTB and identifying key risk factors. These findings provide the potential of ML for enhancing clinical interventions, personalizing prenatal care, and informing public health initiatives.
Related Links	https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-024-06980-4.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712393
DOI	10.1186/s12884-024-06980-4
Journal	BMC Pregnancy and Childbirth
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-12-04
Access Restriction	Open
Subject Keyword	Reproductive Medicine Maternal and Child Health Gynecology Preterm birth Machine learning Prediction model SHAP Multiple pregnancies Threatened abortion
Content Type	Text
Resource Type	Article
Subject	Obstetrics and Gynecology
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3.4/2023

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