洞察撒哈拉以南非洲的长效可逆避孕措施:一个机器学习的角度
Abraham Keffale Mengistu1, Kerebih Getinet2,3, Sefefe Birhanu Tizie1
1Department of Health Informatics, College of Medicine Health Science, Debre Markos University, Debre Markos, Ethiopia.
PloS one
|January 20, 2026
概括
在撒哈拉以南非洲,长效可逆避孕药 (LARC) 的采用率很低. 机器学习将家庭规模和年龄确定为关键预测因素,指导有针对性的干预措施,以增加妇女使用LARC.
科学领域:
- 生殖健康 生殖健康
- 公共卫生 公共卫生
- 机器学习应用 机器学习应用
背景情况:
- 长效可逆避孕药 (LARCs) 对于减少撒哈拉以南非洲 (SSA) 地区意外怀孕和产妇死亡率至关重要.
- 由于系统不平等,文化障碍和医疗保健获得的分散,LARC在SSA的采用率仍然非常低 (8%).
- 需要数据驱动的洞察力来理解和解决LARC吸收率低的问题.
研究的目的:
- 应用机器学习 (ML) 来识别SSA中LARC使用的关键预测因素.
- 引导制定有针对性的干预措施,以增加LARC采用.
主要方法:
- 分析了来自9个南非国家和地区的14275名妇女的全国代表性数据.
- 数据预处理涉及k-NN归算和SMOTEENN类平衡.
- 特性工程包括交互术语,通过SHAP驱动的选择和通过分层交叉验证训练的八个ML模型.
主要成果:
- 随机森林模型在调整和平衡后获得了优异的区分性能 (AUC-ROC: 1.00).
- 确定的主要预测因素是家庭规模 (SHAP=0.464),第一次使用避孕药时的年龄 (0.396) 和当前的年龄 (0.376).
- 拉尔克地区的吸收率极低 (3.3%),农村和城市的差异持续存在;社会文化因素的影响微不足道.
结论:
- 机器学习确定了SSA中LARC干预措施的可操作预测因素.
- 建议包括为大家庭的年轻女性设立移动诊所,为受过教育的人群开展媒体宣传活动.
- 针对青少年的避孕时机教育至关重要,以首次使用年龄为关键预测因素.
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