Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana
Abstract Background Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robust approach to predicting/diagnosing a heal...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-025-07716-8 |
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| author | Frederick Osei Owusu Helena Addai-Manu Esther Serwah Agbedinu Emmanuel Konadu Lydia Asenso Mercy Addae Joseph Osarfo Brenda Abena Ampah Douglas Aninng Opoku |
| author_facet | Frederick Osei Owusu Helena Addai-Manu Esther Serwah Agbedinu Emmanuel Konadu Lydia Asenso Mercy Addae Joseph Osarfo Brenda Abena Ampah Douglas Aninng Opoku |
| author_sort | Frederick Osei Owusu |
| collection | DOAJ |
| description | Abstract Background Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robust approach to predicting/diagnosing a health-related problem, research on their use in determining CS birth is scarce in sub-Saharan Africa. This study therefore aimed to compare the performance of five machine learning techniques in predicting CS birth among pregnant women in a district hospital in Ghana. Methods This was a cross-sectional study that used retrospective data from medical records of pregnant women who delivered at a district hospital in Ghana. A clinical decision support system for predicting CS birth was developed using five machine learning techniques including logistic regression, Support Vector Machines, Naïve Bayes, Random Forest and Extreme Gradient Boosting. Measures such as accuracy, sensitivity, specificity, negative and positive predictive values and area under the receiver operating characteristics curve (AUC-ROC) were used for the model performance. Results Of a total of 2310 deliveries, the prevalence of CS birth was 37.7% with previous CS being the most prevalent indication. The Random Forest model showed the best performance for predicting CS birth with an accuracy of 0.981, recall of 0.994, F1 score of 0.985 and an AUC-ROC of 0.988. The Naïve Bayes model followed with an accuracy of 0.965, recall of 0.967, F1 score of 0.972 and AUC-ROC of 0.986. The top five most important predictors proved to be diastolic (0.0906) and systolic (0.0848) blood pressures, maternal age (0.0756), previous CS (0.0641) and marital status (0.0400). Conclusion This study demonstrated that although all five machine learning techniques had good performance in determining CS births, the Random Forest model was superior to all the others in predicting them. This finding suggests that machine learning could help identify at-risk pregnant women for CS births, potentially supporting early interventions and informing policies in maternal healthcare. |
| format | Article |
| id | doaj-art-b7245a298a914e53aa341e508fe00aca |
| institution | Kabale University |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-b7245a298a914e53aa341e508fe00aca2025-08-20T03:45:39ZengBMCBMC Pregnancy and Childbirth1471-23932025-07-0125111310.1186/s12884-025-07716-8Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in GhanaFrederick Osei Owusu0Helena Addai-Manu1Esther Serwah Agbedinu2Emmanuel Konadu3Lydia Asenso4Mercy Addae5Joseph Osarfo6Brenda Abena Ampah7Douglas Aninng Opoku8Juaben Government HospitalJuaben Government HospitalJuaben Government HospitalDepartment of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and TechnologyJuaben Government HospitalDepartment of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and TechnologyDepartment of Community Health, School of Medicine, University of Health and Allied Health SciencesUniversity Hospital, Kwame Nkrumah University of Science and TechnologyDepartment of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and TechnologyAbstract Background Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robust approach to predicting/diagnosing a health-related problem, research on their use in determining CS birth is scarce in sub-Saharan Africa. This study therefore aimed to compare the performance of five machine learning techniques in predicting CS birth among pregnant women in a district hospital in Ghana. Methods This was a cross-sectional study that used retrospective data from medical records of pregnant women who delivered at a district hospital in Ghana. A clinical decision support system for predicting CS birth was developed using five machine learning techniques including logistic regression, Support Vector Machines, Naïve Bayes, Random Forest and Extreme Gradient Boosting. Measures such as accuracy, sensitivity, specificity, negative and positive predictive values and area under the receiver operating characteristics curve (AUC-ROC) were used for the model performance. Results Of a total of 2310 deliveries, the prevalence of CS birth was 37.7% with previous CS being the most prevalent indication. The Random Forest model showed the best performance for predicting CS birth with an accuracy of 0.981, recall of 0.994, F1 score of 0.985 and an AUC-ROC of 0.988. The Naïve Bayes model followed with an accuracy of 0.965, recall of 0.967, F1 score of 0.972 and AUC-ROC of 0.986. The top five most important predictors proved to be diastolic (0.0906) and systolic (0.0848) blood pressures, maternal age (0.0756), previous CS (0.0641) and marital status (0.0400). Conclusion This study demonstrated that although all five machine learning techniques had good performance in determining CS births, the Random Forest model was superior to all the others in predicting them. This finding suggests that machine learning could help identify at-risk pregnant women for CS births, potentially supporting early interventions and informing policies in maternal healthcare.https://doi.org/10.1186/s12884-025-07716-8AccuracyCaesarean sectionGhanaMachine learningPredictionPregnant women |
| spellingShingle | Frederick Osei Owusu Helena Addai-Manu Esther Serwah Agbedinu Emmanuel Konadu Lydia Asenso Mercy Addae Joseph Osarfo Brenda Abena Ampah Douglas Aninng Opoku Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana BMC Pregnancy and Childbirth Accuracy Caesarean section Ghana Machine learning Prediction Pregnant women |
| title | Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana |
| title_full | Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana |
| title_fullStr | Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana |
| title_full_unstemmed | Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana |
| title_short | Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana |
| title_sort | prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in ghana |
| topic | Accuracy Caesarean section Ghana Machine learning Prediction Pregnant women |
| url | https://doi.org/10.1186/s12884-025-07716-8 |
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