Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015
Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and populati...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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NATURE PORTFOLIO
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/93230 |
| _version_ | 1848765713822515200 |
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| author | Wong, Kingsley Tessema, Gizachew Chai, Kevin Pereira, Gavin |
| author_facet | Wong, Kingsley Tessema, Gizachew Chai, Kevin Pereira, Gavin |
| author_sort | Wong, Kingsley |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification 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 socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (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 specific. |
| first_indexed | 2025-11-14T11:39:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-93230 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:39:38Z |
| publishDate | 2022 |
| publisher | NATURE PORTFOLIO |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-932302023-10-09T08:13:03Z Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 Wong, Kingsley Tessema, Gizachew Chai, Kevin Pereira, Gavin Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics HIGH-RISK OUTCOMES Pregnancy Infant, Newborn Humans Female Premature Birth Western Australia Retrospective Studies Prognosis Risk Factors Cohort Studies Machine Learning Humans Premature Birth Prognosis Risk Factors Retrospective Studies Cohort Studies Pregnancy Infant, Newborn Western Australia Female Machine Learning Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification 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 socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (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 specific. 2022 Journal Article http://hdl.handle.net/20.500.11937/93230 10.1038/s41598-022-23782-w English http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1173991 http://purl.org/au-research/grants/nhmrc/1195716 http://creativecommons.org/licenses/by/4.0/ NATURE PORTFOLIO fulltext |
| spellingShingle | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics HIGH-RISK OUTCOMES Pregnancy Infant, Newborn Humans Female Premature Birth Western Australia Retrospective Studies Prognosis Risk Factors Cohort Studies Machine Learning Humans Premature Birth Prognosis Risk Factors Retrospective Studies Cohort Studies Pregnancy Infant, Newborn Western Australia Female Machine Learning Wong, Kingsley Tessema, Gizachew Chai, Kevin Pereira, Gavin Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title | Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title_full | Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title_fullStr | Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title_full_unstemmed | Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title_short | Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015 |
| title_sort | development of prognostic model for preterm birth using machine learning in a population-based cohort of western australia births between 1980 and 2015 |
| topic | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics HIGH-RISK OUTCOMES Pregnancy Infant, Newborn Humans Female Premature Birth Western Australia Retrospective Studies Prognosis Risk Factors Cohort Studies Machine Learning Humans Premature Birth Prognosis Risk Factors Retrospective Studies Cohort Studies Pregnancy Infant, Newborn Western Australia Female Machine Learning |
| url | http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/93230 |