This preprint is under consideration at BMC Pregnancy and Childbirth. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Alireza Ebrahimvandi
University of Maryland Medical System
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background and Purpose— Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning techniques on big data.
Methods—The 2016 U.S. birth records is obtained and combined with two other area-level datasets, Area Health Resources File and County Health Ranking. Then, we applied multiple machine learning techniques to study a cohort of 3.6 million singleton deliveries to identify generalizable preterm risk factors.
Results—The most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth that can respectively explain 14.91%, 6.92%, and 6.50% of the AUC. Parents education is one of the influential variables in prediction of PTB explaining 10.5% of the AUC. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed other machine learning techniques with an AUC of 0.75 (recall: 0.64, specificity: 0.73) for the validation dataset.
Conclusions—Application of ML techniques improved the performance measures in prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of a preterm birth. More research is needed on the mechanisms through which the socioeconomic factors affect the biological responses.
Keywords
Racial disparities, education, statistical analysis, neural networks, socioeconomic factors
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No competing interests reported.
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CURRENT STATUS: Under Review
Version 1
Posted 03 Mar, 2021
No community comments so far
Reviews received
Received 11 Mar, 2021
Reviewers agreed
On 11 Mar, 2021
Reviewers invited
Invitations sent on 06 Mar, 2021
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On 01 Mar, 2021
Editor invited
On 01 Mar, 2021
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On 01 Mar, 2021
First submitted
On 22 Feb, 2021
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Subject Areas
Maternal & Fetal Medicine
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This preprint is under consideration at BMC Pregnancy and Childbirth. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Alireza Ebrahimvandi
University of Maryland Medical System
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 03 Mar, 2021
No community comments so far
Reviews received
Received 11 Mar, 2021
Reviewers agreed
On 11 Mar, 2021
Reviewers invited
Invitations sent on 06 Mar, 2021
Editor assigned
On 01 Mar, 2021
Editor invited
On 01 Mar, 2021
Submission checks complete
On 01 Mar, 2021
First submitted
On 22 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Maternal & Fetal Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and Purpose— Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning techniques on big data.
Methods—The 2016 U.S. birth records is obtained and combined with two other area-level datasets, Area Health Resources File and County Health Ranking. Then, we applied multiple machine learning techniques to study a cohort of 3.6 million singleton deliveries to identify generalizable preterm risk factors.
Results—The most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth that can respectively explain 14.91%, 6.92%, and 6.50% of the AUC. Parents education is one of the influential variables in prediction of PTB explaining 10.5% of the AUC. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed other machine learning techniques with an AUC of 0.75 (recall: 0.64, specificity: 0.73) for the validation dataset.
Conclusions—Application of ML techniques improved the performance measures in prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of a preterm birth. More research is needed on the mechanisms through which the socioeconomic factors affect the biological responses.
Figure 1
Figure 2
Figure 3
This preprint is available for download as a PDF.
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