使用机器学习方法预测高血压并确定孟加拉国已婚妇女中最重要的因素
Novel Chandra Das1, Probir Kumar Ghosh1, Md Alamgir Hossain1
1International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
PloS one
|October 30, 2025
概括
机器学习模型使用社会人口统计和行为数据准确预测孟加拉妇女的高血压. 关键的风险因素包括年龄较大,较高的平价率和肥胖,指导有针对性的公共卫生干预.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 心血管健康 心血管健康
背景情况:
- 高血压在孟加拉国对孕产妇和心脏代谢健康问题有很大影响.
- 机器学习 (ML) 模型可以预测高血压,并识别低资源环境中的风险因素.
研究的目的:
- 开发和解释孟加拉国已婚妇女高血压预测的ML模型.
- 确定与高血压相关的关键因素,用于有针对性的查和政策制定.
主要方法:
- 来自BDHS 2017-18年调查的4253名已婚妇女的分析.
- 培训和验证12ML算法与类平衡策略.
- SHAP用于排名顶级预测器和可视化特征效应.
主要成果:
- 使用SMOTE+ENN的额外树木分类器实现了最佳性能 (F1=0.94,AUC-PR=0.95,ROC-AUC=0.95).
- 确定了关键预测因素:年龄,性别平等,最近出生,使用避孕药,配偶教育和BMI.
- 保护因素:年龄较小 (<35) 和正常/体重不足状态. 危险因素:夫妻平等率≥2-3,丈夫年龄≥40,超重/肥胖.
结论:
- 使用社会人口统计,行为和有限的生物识别数据的可解释的ML模型准确地识别了高血压风险.
- 研究结果支持将风险评分纳入电子注册和例行医疗检查.
- 建议包括以丈夫为重点的干预措施,并优先考虑高风险地区的高平价家庭.
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