ArticlePloS one2023
Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Association of Maternal Cardiac Arrhythmias with Pregnancy Outcomes: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence applications in health insurances: a scoping review.Cost effectiveness and resource allocation : C/E · 2025Review
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.BMC pregnancy and childbirth · 2024Article
- The Challenge of Managing Atrial Fibrillation during Pregnancy.Reviews in cardiovascular medicine · 2023Review
- Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMaternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.
methodsA population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.
resultsAmong the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.
conclusionsCareful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.
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