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在11个撒哈拉以南非洲国家使用人口和健康调查数据对女性生殖器切割进行基于机器学习的分类

Alemayehu Kasu Gebrehana¹, Lidetu Demoze², Gelila Yitageasu²

¹Department of Midwifery, College of Health sciences, Salale University, Fitche, Ethiopia. alemayehukasu3@gmail.com.

Scientific reports

|February 19, 2026

概括

这项研究使用机器学习对撒哈拉以南非洲地区的女性生殖器切割 (FGM) 进行了分类,发现随机森林模型最有效. 影响分类的关键因素包括母亲的观点,国家和宗教理由.

科学领域:

公共卫生公共卫生
机器学习应用机器学习应用
人口健康调查人口健康调查

背景情况:

切割女性生殖器官 (FGM) 与严重的健康问题有关,包括不孕症和增加的孕产妇死亡率.
撒哈拉以南非洲 (SSA) 缺乏将机器学习 (ML) 与全国代表的人口和健康调查 (DHS) 数据用于FGM分类的研究.

研究的目的:

通过使用DHS数据,确定在SSA中对FGM进行分类的最有效的ML模型.
为了利用ML处理复杂数据集的能力,与传统方法相比,更准确的FGM分类.

主要方法:

利用来自11个SAS国家 (2015-2023) 的二级DHS数据,对62,249名至少有一个女儿的妇女进行了分析.
应用了七个分类算法:物流回归,决策树,随机森林 (RF),SVM,KNN,XGBoost和Naive Bayes.
使用SHAP图表来解释特征,以了解FGM分类驱动因素.

主要成果:

随机森林分类器实现了最高的性能,准确度为0.85,精度为0.83,回忆率为0.88和F1得分为0.85.
母亲对FGM的看法,居住国,受访者的割礼地位和宗教理由是分类中最重要的特征.
射频和决策树模型显示出最佳的校准.

结论:

随机森林模型对于使用DHS数据在SSA中对FGM进行分类是非常有效的.
干预措施应优先考虑教育和宣传,针对母亲的意见和FGM的宗教理由.
社区参与对于挑战植根于文化或宗教规范的FGM做法至关重要.

关键词:

分类分类分类分类.切割女性生殖器官的方法机器学习机器学习撒哈拉以南非洲地区.

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