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Machine learning model-based preterm birth prediction and clinical nomogram: A big retrospective cohort study.
Ya Liu1, Jiangling Liu1, Heqing Shen2
1State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory and State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.
Summary
This study created a reliable tool to predict preterm birth risk using machine learning and large datasets. The model identifies key risk factors, aiding clinicians in early prevention strategies.
Area of Science:
- Reproductive Health
- Data Science in Medicine
- Clinical Prediction Modeling
Background:
- Preterm birth remains a leading cause of neonatal mortality and morbidity worldwide.
- Existing predictive models often lack robust external validation, limiting their clinical applicability.
- Early identification of at-risk pregnancies is crucial for implementing timely interventions.
Purpose of the Study:
- To develop and validate a multifactorial predictive model for preterm birth risk.
- To identify key risk factors associated with preterm birth using machine learning algorithms.
- To provide clinicians with a tool for early prevention of preterm birth.
Main Methods:
- Retrospective cohort study using National Vital Statistics System (NVSS) birth data (2022 and 2018).
- Four machine learning algorithms (logistic regression, adaptive lasso, bootstrap forest, boosted trees) were employed for feature identification.
- A logistic regression-based nomogram was constructed using consensus features and validated internally and externally.
Main Results:
- The study analyzed over 5.2 million mother-infant pairs.
- Machine learning models achieved high accuracy (AUC > 0.7) in preterm birth prediction.
- The developed nomogram demonstrated consistent performance across training, internal, and external validation sets (AUCs ~0.70).
Conclusions:
- A reliable and externally validated preterm birth prediction tool was successfully developed.
- The model effectively identified nine common risk factors for preterm birth.
- This tool addresses the need for validated prediction models to support clinical decision-making and early prevention efforts.
