Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a singl...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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NATURE PORTFOLIO
2020
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| Online Access: | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/90949 |
| _version_ | 1848765469531570176 |
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| author | Malacova, Eva Tippaya, Sawitchaya Bailey, Helen Chai, Kevin Farrant, B.M. Gebremedhin, Amanuel Leonard, H. Marinovich, Luke Nassar, N. Phatak, Aloke Raynes-Greenow, C. Regan, Annette Shand, A.W. Shepherd, Carrington Srinivasjois, Ravisha Tessema, Gizachew Pereira, Gavin |
| author_facet | Malacova, Eva Tippaya, Sawitchaya Bailey, Helen Chai, Kevin Farrant, B.M. Gebremedhin, Amanuel Leonard, H. Marinovich, Luke Nassar, N. Phatak, Aloke Raynes-Greenow, C. Regan, Annette Shand, A.W. Shepherd, Carrington Srinivasjois, Ravisha Tessema, Gizachew Pereira, Gavin |
| author_sort | Malacova, Eva |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression. |
| first_indexed | 2025-11-14T11:35:45Z |
| format | Journal Article |
| id | curtin-20.500.11937-90949 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:45Z |
| publishDate | 2020 |
| publisher | NATURE PORTFOLIO |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909492023-05-04T08:13:55Z Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 Malacova, Eva Tippaya, Sawitchaya Bailey, Helen Chai, Kevin Farrant, B.M. Gebremedhin, Amanuel Leonard, H. Marinovich, Luke Nassar, N. Phatak, Aloke Raynes-Greenow, C. Regan, Annette Shand, A.W. Shepherd, Carrington Srinivasjois, Ravisha Tessema, Gizachew Pereira, Gavin Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics Algorithms Cohort Studies Female Humans Live Birth Machine Learning Maternal Age Pregnancy Pregnancy Complications Prenatal Care Reproductive History Risk Assessment Socioeconomic Factors Stillbirth Western Australia Humans Pregnancy Complications Reproductive History Prenatal Care Risk Assessment Cohort Studies Maternal Age Pregnancy Algorithms Socioeconomic Factors Western Australia Female Stillbirth Live Birth Machine Learning Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression. 2020 Journal Article http://hdl.handle.net/20.500.11937/90949 10.1038/s41598-020-62210-9 English http://purl.org/au-research/grants/arc/IC180100030 http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1173991 http://creativecommons.org/licenses/by/4.0/ NATURE PORTFOLIO fulltext |
| spellingShingle | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics Algorithms Cohort Studies Female Humans Live Birth Machine Learning Maternal Age Pregnancy Pregnancy Complications Prenatal Care Reproductive History Risk Assessment Socioeconomic Factors Stillbirth Western Australia Humans Pregnancy Complications Reproductive History Prenatal Care Risk Assessment Cohort Studies Maternal Age Pregnancy Algorithms Socioeconomic Factors Western Australia Female Stillbirth Live Birth Machine Learning Malacova, Eva Tippaya, Sawitchaya Bailey, Helen Chai, Kevin Farrant, B.M. Gebremedhin, Amanuel Leonard, H. Marinovich, Luke Nassar, N. Phatak, Aloke Raynes-Greenow, C. Regan, Annette Shand, A.W. Shepherd, Carrington Srinivasjois, Ravisha Tessema, Gizachew Pereira, Gavin Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title | Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title_full | Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title_fullStr | Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title_full_unstemmed | Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title_short | Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015 |
| title_sort | stillbirth risk prediction using machine learning for a large cohort of births from western australia, 1980–2015 |
| topic | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics Algorithms Cohort Studies Female Humans Live Birth Machine Learning Maternal Age Pregnancy Pregnancy Complications Prenatal Care Reproductive History Risk Assessment Socioeconomic Factors Stillbirth Western Australia Humans Pregnancy Complications Reproductive History Prenatal Care Risk Assessment Cohort Studies Maternal Age Pregnancy Algorithms Socioeconomic Factors Western Australia Female Stillbirth Live Birth Machine Learning |
| url | http://purl.org/au-research/grants/arc/IC180100030 http://purl.org/au-research/grants/arc/IC180100030 http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/90949 |