Ngusie Habtamu Setegn, Tesfa Getanew Aschalew, Taddese Asefa Adimasu, Enyew Ermias Bekele, Alene Tilahun Dessie, Abebe Gebremeskel Kibret, Walle Agmasie Damtew, Zemariam Alemu Birara
Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia.
School of Public Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia.
Front Public Health. 2024 Nov 27;12:1439320. doi: 10.3389/fpubh.2024.1439320. eCollection 2024.
Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women's choices of delivery locations in East Africa.
The study focused on 86,009 childbearing women in East Africa. A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.
The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. Factors associated with facility-based deliveries were identified using association rule mining, including parental education levels, timing of initial antenatal care (ANC) check-ups, wealth status, marital status, mobile phone ownership, religious affiliation, media accessibility, and birth order.
This study underscores the vital role of machine learning algorithms in predicting health facility deliveries. A slight decline in facility deliveries from previous reports highlights the urgent need for targeted interventions to meet Sustainable Development Goals (SDGs), particularly in maternal health. The study recommends promoting facility-based deliveries. These include raising awareness about skilled birth attendance, encouraging early ANC check-up, addressing financial barriers through targeted support programs, implementing culturally sensitive interventions, utilizing media campaigns, and mobile health initiatives. Design specific interventions tailored to the birth order of the child, recognizing that mothers may have different informational needs depending on whether it is their first or subsequent delivery. Furthermore, we recommended researchers to explore a variety of techniques and validate findings using more recent data.
由于分娩期间获得熟练医疗保健的机会有限，撒哈拉以南非洲面临着较高的新生儿和孕产妇死亡率。本研究旨在利用先进的机器学习技术改进卫生设施和家庭分娩的分类，并探索影响东非妇女分娩地点选择的因素。
该研究聚焦于东非的86,009名育龄妇女。对12种先进的机器学习算法进行了比较分析，采用了各种数据平衡技术和超参数优化方法来提高模型性能。
发现东非卫生设施分娩的患病率为83.71%。研究结果表明，支持向量机（SVM）算法和CatBoost在预测分娩地点方面表现最佳，经过贝叶斯优化调整后，这两种算法的准确率均为95%，曲线下面积（AUC）为0.98，在所有指标性能的综合分析中两者无显著差异。使用关联规则挖掘确定了与设施分娩相关的因素，包括父母教育水平、首次产前检查（ANC）的时间、财富状况、婚姻状况、手机拥有情况、宗教信仰、媒体可及性和出生顺序。
本研究强调了机器学习算法在预测卫生设施分娩方面的重要作用。与之前的报告相比，设施分娩略有下降，这凸显了采取针对性干预措施以实现可持续发展目标（SDGs）的迫切需求，特别是在孕产妇健康方面。该研究建议促进设施分娩。这些措施包括提高对熟练助产服务的认识、鼓励早期进行产前检查、通过有针对性的支持计划解决经济障碍、实施具有文化敏感性的干预措施、利用媒体宣传活动和移动健康倡议。针对孩子的出生顺序设计特定的干预措施，因为要认识到母亲根据是头胎还是后续分娩可能有不同的信息需求。此外，我们建议研究人员探索各种技术，并使用更新的数据验证研究结果。
