Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques

Abstract Background Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction. Methods The study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS). We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability. Results Based on our findings, about 28.59% (95% CI: 28.36, 28.83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88.0%. The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region. Conclusion The region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies risky groups, including first-time mothers, women with short or long birth intervals, and those with unwanted pregnancies. To address the needs of these high-risk women, the researchers recommend immediately providing iron supplements, scheduling comprehensive prenatal care, and strongly encouraging facility-based deliveries or skilled birth attendance.

背景：不良妊娠结局（包括早产、低出生体重儿及死产）仍是全球主要公共卫生挑战，在欠发达地区尤为突出。明确潜在风险因素，对于制定针对妊娠结局的有效干预措施至关重要。据此，本研究旨在借助先进机器学习技术，构建撒哈拉以南非洲育龄妇女不良妊娠结局预测模型。此外，本研究拟采用创新性数据科学可解释性技术，识别核心风险因素，并量化各特征对模型预测结果的影响程度。方法：本研究的研究人群为26个撒哈拉以南非洲国家的育龄妇女，且均在数据采集前5年内完成分娩，总样本量达139659例。数据来源于最新版人口与健康调查（Demographic Health Survey, DHS）。研究采用多种数据平衡技术，纳入10种先进机器学习算法，将数据集按80%训练集、20%测试集的比例划分。通过多种性能指标结合超参数优化方法开展模型评估，并借助关联规则挖掘与SHAP分析（SHapley Additive exPlanations）提升模型可解释性。结果：研究结果显示，撒哈拉以南非洲约28.59%（95%置信区间：28.36, 28.83）的育龄妇女出现不良妊娠结局。经反复实验与评估，随机森林（Random Forest）模型表现最优，其曲线下面积（AUC）达0.95，准确率为88.0%。本次研究识别的核心风险因素包括：在家分娩、未接受产前铁剂补充、产前检查（Antenatal Care, ANC）次数少于4次、分娩间隔过短或过长、非意愿妊娠、初产妇以及居住于西非地区。结论：该地区不良妊娠结局负担仍持续存在，凸显了加大关注与行动的紧迫性。值得欣喜的是，先进机器学习方法（尤以随机森林算法为代表）为靶向干预提供了关键洞见。具体而言，分析明确了高危人群类别，包括初产妇、分娩间隔异常的妇女以及非意愿妊娠女性。为满足此类高风险妇女的健康需求，研究人员建议立即开展产前铁剂补充、规范产前检查流程，并大力推广住院分娩或由专业人员接产。