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机器学习的应用用于预测撒哈拉以南非洲妇女的延迟生育能力

Meron Asmamaw Alemayehu¹, Nebiyu Mekonnen Derseh¹, Tigist Kifle Tsegaw²

¹Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

Reproduction & fertility

|September 16, 2025

概括

延迟生育能力影响超过30%的女性,年龄,婚姻状况和生育治疗是关键预测因素. 机器学习模型准确地识别了面临风险的个人,以提供有针对性的支持.

科学领域:

生殖健康生殖健康
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

延迟生育能力,定义为≥12个月的未成功受孕尝试,是影响生育率的日益严重的全球问题.
了解延迟生育能力的预测因素对于及时干预和支持至关重要.

研究的目的:

使用机器学习模型预测延迟的生育能力.
确定影响撒哈拉以南非洲延迟生育能力的关键预测因素.

主要方法:

利用了来自五个撒哈拉以南非洲国家的绩效监测行动 (PMA) 调查的二次数据.
采用Boruta算法进行特征选择,并通过交叉验证的网格搜索优化随机森林,XGBoost和LightGBM模型.
应用了SHapley添加式解释 (SHAP) 来进行模型解释性和子组分析,以探索异质性.

主要成果:

在31.01%的女性中观察到延迟生育能力.
随机森林模型获得了最高的准确性 (79.2%) 和AUC (0.94).
确定的主要预测因素包括年龄 (36-49岁),婚姻状况,诱发排卵的治疗和草药的使用.

结论:

机器学习模型,特别是随机森林,可以有效地预测延迟的生育能力.
年龄,婚姻状况和生育治疗史是重要的预测因素,在不同小组中具有不同的影响.
建议进行量身定制的生育咨询和有针对性的查,特别是对于之前接受过生育治疗的夫妇.

关键词:

关于PMA数据的数据在SHAP分析中,我们分析了SHAP.延迟的生育能力延迟的生育能力不孕症不孕不育不孕不育不孕不育可解释的人工智能AI 机器学习是机器学习.生殖健康生殖健康小组分析小组分析

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