通过可解释机器学习识别乌干达使用熟练分娩服务的预测因素
Shaheen M Z Memon1, Robert Wamala2, Ignace H Kabano1
1African Centre of Excellence in Data Science, College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda.
International journal of environmental research and public health
|November 27, 2025
概括
机器学习确定了影响乌干达熟练分娩 (SBA) 的因素. 教育,产前护理和地区是改善孕产妇健康结果的关键预测因素.
科学领域:
- 公共卫生 公共卫生
- 机器学习应用 机器学习应用
- 孕产妇健康 孕产妇健康
背景情况:
- 熟练的分娩服务 (SBA) 对于降低产妇和新生儿死亡率至关重要.
- 在许多低收入和中等收入国家,包括乌干达,获得SBA是有限的.
研究的目的:
- 用机器学习预测乌干达妇女使用熟练分娩服务 (SBA).
- 确定影响SBA利用的关键因素.
主要方法:
- 分析2016年乌干达人口与健康调查数据.
- 应用六种基于树的机器学习模型和物流回归.
- 使用成本敏感学习来处理类不平衡和贝叶斯优化来对超参数进行调整.
主要成果:
- XGBoost模型显示出优异的性能 (F1得分:0.52,AUC:0.75).
- 确定的主要预测因素包括教育水平,产前护理访问,地区 (乌干达北部),感知到设施的距离和居住类型.
- 沙普利添加式解释 (SHAP) 提供了模型的解释性.
结论:
- 可解释机器学习有效地识别出低SBA使用风险的人群.
- 结果可以指导有针对性的干预措施,以改善乌干达的孕产妇健康服务.
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