Kubahoniyesu Theogene, Kabano Ignace Habimana
African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda.
Research, Innovation and Data Science, Rwanda Biomedical Centre, Kigali, Rwanda.
PLoS One. 2024 Dec 5;19(12):e0312447. doi: 10.1371/journal.pone.0312447. eCollection 2024.
Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms.
This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019-2020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority oversampling technique (SMOTE) were used to manage dataset partitioning and class imbalance. Descriptive and multivariate analyses were conducted to identify the prevalence and risk factors for adverse pregnancy outcomes. Seven machine learning algorithms were assessed for their accuracy, precision, recall, F1 score, and area under the curve (AUC).
Of the pregnancies analyzed, 93.4% resulted in live births, while 4.5% ended in miscarriage, and 2.1% in stillbirth. Advanced maternal age(>30 years),women aged 30-34 years (adjusted odds ratio [AOR] = 5.755; 95% confidence interval [CI] = 3.085-10.074; p < 0.001), 35-39 years (AOR = 8.458; 95% CI = 4.507-10.571; p < 0.001), 40-44 years (AOR = 11.86; 95% CI = 6.250-21.842; p < 0.001), and 45-49 years (AOR = 14.233; 95% CI = 7.359-25.922; p < 0.001), compared to those aged 15-19 years, and multiple unions (polyandry) (AOR = 1.320; 95% CI = 1.104-1.573, p = 0.002), and women not visited by healthcare provider during pregnancy (AOR = 1.421; 95%CI = 1.300-1.611, p<0.001) were factors associated with an increased risk of adverse pregnancy outcomes. In contrast, being married (AOR = 0.894; 95% CI = 0.787-0.966) and attending at least two antenatal care (ANC) visits (AOR = 0.801; 95% CI = 0.664-0.961) were linked to reduced risk. The K-nearest neighbors (KNN) model outperformed other ML Models in predicting adverse pregnancy outcomes, achieving 86% accuracy, 89% precision, 97% recall, 93% F1 score, and an area under the curve (AUC) of 0.842. The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes.
Machine learning algorithms, particularly KNN, are effective in predicting adverse pregnancy outcomes, facilitating early intervention and improved maternal and neonatal care.
不良妊娠结局对孕产妇和新生儿健康构成重大风险，会导致发病、死亡以及长期的发育挑战。本研究旨在使用监督机器学习算法预测卢旺达的这些结局。
这项横断面研究利用了卢旺达人口与健康调查（RDHS，2019 - 2020年）的数据，涉及14,634名妇女。采用K折交叉验证（k = 10）和合成少数过采样技术（SMOTE）来管理数据集划分和类别不平衡。进行描述性和多变量分析以确定不良妊娠结局的患病率和风险因素。评估了七种机器学习算法的准确性、精确性、召回率、F1分数和曲线下面积（AUC）。
在分析的妊娠中，93.4%为活产，4.5%以流产告终，2.1%为死产。与15 - 19岁的妇女相比，高龄产妇（>30岁）、30 - 34岁的妇女（调整后的优势比[AOR] = 5.755；95%置信区间[CI] = 3.085 - 10.074；p < 0.001）、35 - 39岁的妇女（AOR = 8.458；95% CI = 4.507 - 10.571；p < 0.001）、40 - 44岁的妇女（AOR = 11.86；95% CI = 6.250 - 21.842；p < 0.001）以及45 - 49岁的妇女（AOR = 14.233；95% CI = 7.359 - 25.922；p < 0.001），以及多个配偶关系（一妻多夫制）（AOR = 1.320；95% CI = 1.104 - 1.573，p = 0.002），和孕期未接受医疗保健提供者访视的妇女（AOR = 1.421；95% CI = 1.300 - 1.611，p < 0.001）是与不良妊娠结局风险增加相关的因素。相比之下，已婚（AOR = 0.894；95% CI = 0.787 - 0.966）和至少进行两次产前检查（ANC）（AOR = 0.801；95% CI = 0.664 - 0.961）与风险降低相关。K近邻（KNN）模型在预测不良妊娠结局方面优于其他机器学习模型，准确率达到86%，精确率为89%，召回率为97%，F1分数为93%，曲线下面积（AUC）为0.842。机器学习模型不断强调，高龄产妇、处于多个配偶关系中的妇女以及产前检查不足的妇女更容易出现不良妊娠结局。
机器学习算法，特别是KNN，在预测不良妊娠结局方面是有效的，有助于早期干预并改善孕产妇和新生儿护理。
