An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers.
The desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular delivery mode is supposedly influenced by a numb...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-02-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000543 |
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| author | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Abdul-Salaam Gaddafi |
| author_facet | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Abdul-Salaam Gaddafi |
| author_sort | Michael Owusu-Adjei |
| collection | DOAJ |
| description | The desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular delivery mode is supposedly influenced by a number of factors that leads to the ultimate decision of choice. Some of the factors identified include maternal birth history, maternal and child health conditions prevailing before and during labor onset. Predictive modeling has been used extensively to determine important contributory factors or artifacts influencing delivery choice in related research studies. However, missing among a myriad of features used in various research studies for this determination is maternal history of spontaneous, threatened and inevitable abortion(s). How its inclusion impacts delivery outcome has not been covered in extensive research work. This research work therefore takes measurable maternal features that include real time information on administered partographs to predict delivery outcome. This is achieved by adopting effective feature selection technique to estimate variable relationships with the target variable. Three supervised learning techniques are used and evaluated for performance. Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. Correlation statistic for variable independence among input variables showed that delivery outcome type as an output is associated with fetal gestational age and the progress of maternal cervix dilatation during labor onset. |
| format | Article |
| id | doaj-art-dbd084a78827489b8eb9e08f3e2d58be |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-dbd084a78827489b8eb9e08f3e2d58be2025-02-12T05:31:24ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-02-0142e000054310.1371/journal.pdig.0000543An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers.Michael Owusu-AdjeiJames Ben Hayfron-AcquahTwum FrimpongAbdul-Salaam GaddafiThe desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular delivery mode is supposedly influenced by a number of factors that leads to the ultimate decision of choice. Some of the factors identified include maternal birth history, maternal and child health conditions prevailing before and during labor onset. Predictive modeling has been used extensively to determine important contributory factors or artifacts influencing delivery choice in related research studies. However, missing among a myriad of features used in various research studies for this determination is maternal history of spontaneous, threatened and inevitable abortion(s). How its inclusion impacts delivery outcome has not been covered in extensive research work. This research work therefore takes measurable maternal features that include real time information on administered partographs to predict delivery outcome. This is achieved by adopting effective feature selection technique to estimate variable relationships with the target variable. Three supervised learning techniques are used and evaluated for performance. Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. Correlation statistic for variable independence among input variables showed that delivery outcome type as an output is associated with fetal gestational age and the progress of maternal cervix dilatation during labor onset.https://doi.org/10.1371/journal.pdig.0000543 |
| spellingShingle | Michael Owusu-Adjei James Ben Hayfron-Acquah Twum Frimpong Abdul-Salaam Gaddafi An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. PLOS Digital Health |
| title | An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. |
| title_full | An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. |
| title_fullStr | An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. |
| title_full_unstemmed | An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. |
| title_short | An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. |
| title_sort | ai based approach to predict delivery outcome based on measurable factors of pregnant mothers |
| url | https://doi.org/10.1371/journal.pdig.0000543 |
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