解释健康差异的机器学习:我们所有人研究计划中的2型糖尿病
Manoj S Kambara1, Onyinye Chukka2, Kathryn J Choi1
1National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.
bioRxiv : the preprint server for biology
|March 3, 2025
概括
可解释的机器学习确定了导致健康差异的关键2型糖尿病 (T2D) 风险因素. 收入,腰围和教育显著影响了种族和种族群体之间的T2D流行率差异.
科学领域:
- 计算流行病学计算流行病学
- 健康差异研究 研究健康差异研究
- 机器学习在公共卫生中的应用.
背景情况:
- 2型糖尿病 (T2D) 呈现出显著的发病率和死亡率,不成比例地影响少数群体.
- 现有的关于T2D风险因素的研究尚未充分解决健康差异.
- 机器学习 (ML) 为复杂的健康数据提供了先进的分析能力.
研究的目的:
- 采用可解释的ML方法来发现和描述T2D健康差异风险因素.
- 量化T2D风险因素对不同自我认同的种族和种族 (SIRE) 群体的群体特异性影响.
主要方法:
- 应用了SHapley添加式扩展 (SHAP),一种可解释的ML技术,用于分析T2D风险模型.
- 开发了ML分类器 (随机森林,轻GBM,XGBoost) 以在SIRE组内和之间建模T2D风险.
- 通过SIRE的分层SHAP值来评估风险因素对T2D患病率的差异影响.
主要成果:
- ML模型准确地预测了T2D风险,并复制了SIRE组间观察到的流行率差异.
- 在所有SIRE组中,前七个T2D风险因素一致,但其重要性顺序不同.
- SHAP分析显示,收入,腰围和教育程度解释了黑人/非洲裔美国人与黑人/非洲裔美国人较高的T2D患病率. 他们是白人.
- 收入,教育和甘油三是解释西班牙裔/拉丁裔群体T2D患病率较高的关键因素.
结论:
- 可解释的ML,特别是SHAP,有效地阐明了T2D健康差异风险因素.
- 这种方法量化了风险因素对T2D流行率差异的特定组贡献.
- 这些发现突显了可解释的ML在解决T2D的健康不平等方面的潜力.
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