Owusu Frederick Osei, Addai-Manu Helena, Agbedinu Esther Serwah, Konadu Emmanuel, Asenso Lydia, Addae Mercy, Osarfo Joseph, Ampah Brenda Abena, Opoku Douglas Aninng
Juaben Government Hospital, Juaben, Ghana.
Onwe Government Hospital, Onwe, Ghana.
BMC Pregnancy Childbirth. 2025 Jul 2;25(1):690. doi: 10.1186/s12884-025-07716-8.
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.
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.
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).
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.
机器学习算法可能有助于改善母婴健康，包括在资源匮乏国家确定剖宫产（CS）分娩的适宜性。尽管机器学习算法为预测/诊断健康相关问题提供了一种更强大的方法，但在撒哈拉以南非洲，关于其在确定CS分娩中的应用研究却很匮乏。因此，本研究旨在比较五种机器学习技术在加纳一家区级医院预测孕妇CS分娩的性能。
这是一项横断面研究，使用了加纳一家区级医院分娩的孕妇病历中的回顾性数据。利用逻辑回归、支持向量机、朴素贝叶斯、随机森林和极端梯度提升这五种机器学习技术，开发了一个预测CS分娩的临床决策支持系统。模型性能采用准确率、灵敏度、特异性、阴性和阳性预测值以及受试者操作特征曲线下面积（AUC-ROC）等指标。
在总共2310例分娩中，CS分娩的患病率为37.7%，既往剖宫产是最常见的指征。随机森林模型在预测CS分娩方面表现最佳，准确率为0.981，召回率为0.994，F1分数为0.985，AUC-ROC为0.988。朴素贝叶斯模型紧随其后，准确率为0.965，召回率为0.967，F1分数为0.972，AUC-ROC为0.986。最重要的五个预测因素被证明是舒张压（0.0906）和收缩压（0.0848）、产妇年龄（0.0756）、既往剖宫产（0.0641）和婚姻状况（0.0400）。
本研究表明，尽管所有五种机器学习技术在确定CS分娩方面都有良好表现，但随机森林模型在预测方面优于其他所有模型。这一发现表明，机器学习可以帮助识别有CS分娩风险的孕妇，可能支持早期干预并为孕产妇保健政策提供信息。
