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时间趋势和基于机器学习的女性不孕症风险预测:使用NHANES数据 (2015-2023) 进行交叉队列分析

Ismat Ara Begum¹, Deepak Ghimire², A S M Sanwar Hosen³

¹Department of Biomedical Sciences and Institute for Medical Science, Jeonbuk National University Medical School, Jeonju 54907, Republic of Korea.

Diagnostics (Basel, Switzerland)

|September 13, 2025

概括

女性不孕症的患病率显著上升,可能是由于流行后的影响. 机器学习模型使用关键临床因素准确预测不孕症风险,帮助未来的生殖健康战略.

科学领域:

生殖健康和流行病学
生物统计学和机器学习

背景情况:

女性不孕症是全球主要的健康问题,其趋势和风险因素尚未得到充分研究.
关于不孕症患病率和预测模型的全国代表性数据有限.

研究的目的:

调查女性不育率的时间趋势.
用临床特征评估机器学习 (ML) 模型来预测不孕症风险.

主要方法:

从NHANES周期 (2015-2023) 中对19-45岁的美国女性进行分析,并提供完整的不孕症相关数据.
使用ANOVA和Chi-square测试进行不孕症患病率的比较.
使用GridSearchCV和交叉验证开发和验证六个ML模型 (LR,RF,XGBoost,NB,SVM,堆叠).

主要成果:

不孕症的患病率从14.8% (2017-2018) 增加到27.8% (2021-2023).
以前的分娩是保护性的 (调整的OR ≈0.00);月经不规则与不孕症有关 (OR=0.55).
机器学习模型在最小的特征集下实现了高预测性能 (AUC>0.96).

结论:

美国女性不孕率的上升凸显了迫切的公共卫生问题.
可解释和集体ML模型有效预测不孕症风险,支持监测和个性化护理.
未来的研究应该纳入更广泛的社会人口统计学和行为因素,以提高准确性.

关键词:

尼汉斯 (NHANES) 是一个名人.在ROC曲线上,ROC曲线女性不孕症女性不孕症逻辑回归的逻辑回归方法机器学习是机器学习.生殖健康生殖健康风险预测风险预测

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