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可解释机器学习用于心血管风险预测:从NHANES饮食和健康数据的洞察力
Md Ahiduzzaman1, Md Nahid Hasan2
1Department of Statistics and Data Science, University of Central Florida, Orlando, Florida, United States of America.
透明的机器学习模型使用NHANES数据准确预测心血管疾病 (CVD) 风险. 关键预测因素包括年龄,维生素B12和C反应性蛋白,强调营养.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 营养科学 营养科学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,需要准确的早期预测.
- 国家健康和营养检查调查 (NHANES) 为调查心血管疾病风险因素提供了丰富的数据集.
研究的目的:
- 开发透明的机器学习 (ML) 模型来预测心血管疾病风险.
- 使用NHANES数据 (2017-2023) 识别导致心脏病的关键饮食和健康因素.
主要方法:
- 分析了来自NHANES (2017-2023) 的12,382名成年参与者,其中包括41个变量.
- 使用递归特征消除 (RFE) 进行特征选择,并使用ROSE处理类失衡.
- 训练和评估五个ML模型 (逻辑回归,随机森林,SVM,XGBoost,LightGBM) 与LIME和SHAP进行解释性.
主要成果:
- 在心血管疾病和非心血管疾病组之间,年龄,腰围,血压,CRP和营养摄入量存在显著差异.
- XGBoost模型实现了最高的精度 (0.8216) 和回忆 (0.8645);随机森林具有最高的AUROC (0.8139).
- 确定了关键预测因素:年龄,维生素B12,总胆固醇,CRP和腰围.
结论:
- 可解释的ML模型成功地确定了关键的饮食和临床心血管疾病风险因素.
- 像维生素B12和尼亚这样的营养素,以及临床标志物,是重要的预测因素.
- 研究结果支持营养干预在预防心血管疾病的公共卫生战略中的作用.
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