Current studentsExisting staffContact us
 

Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015

Simple item page
dc.contributor.authorWong, K.
dc.contributor.authorTessema, G.A.
dc.contributor.authorChai, K.
dc.contributor.authorPereira, G.
dc.date.issued2022
dc.description.abstractPreterm birth is a global public health problem with a signifcant burden on the individuals afected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classifcation algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal sociodemographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratifed tenfold cross-validation was used to reduce overftting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classifed by the best performing classifer (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time speciifc.
dc.description.statementofresponsibilityKingsley Wong, Gizachew A.Tessema, Kevin Chai, Gavin Pereira
dc.identifier.citationScientific Reports, 2022; 12(1):1-16
dc.identifier.doi10.1038/s41598-022-23782-w
dc.identifier.issn2045-2322
dc.identifier.issn2045-2322
dc.identifier.orcidTessema, G.A. [0000-0002-4784-8151]
dc.identifier.urihttps://hdl.handle.net/2440/136927
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1099655
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1195716
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1173991
dc.rights© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.source.urihttps://doi.org/10.1038/s41598-022-23782-w
dc.subjectHumans
dc.subjectPremature Birth
dc.subjectPrognosis
dc.subjectRisk Factors
dc.subjectRetrospective Studies
dc.subjectCohort Studies
dc.subjectPregnancy
dc.subjectInfant, Newborn
dc.subjectWestern Australia
dc.subjectFemale
dc.subjectMachine Learning
dc.subject.meshHumans
dc.subject.meshPremature Birth
dc.subject.meshPrognosis
dc.subject.meshRisk Factors
dc.subject.meshRetrospective Studies
dc.subject.meshCohort Studies
dc.subject.meshPregnancy
dc.subject.meshInfant, Newborn
dc.subject.meshWestern Australia
dc.subject.meshFemale
dc.subject.meshMachine Learning
dc.titleDevelopment of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_136927.pdf
Size:
1.7 MB
Format:
Adobe Portable Document Format
Download

Collections

Public Health publications