预测心血管疾病死亡率:利用机器学习全面评估健康和营养变量
Agustin Martin-Morales1,2, Masaki Yamamoto1,2, Mai Inoue1,2
1Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan.
机器学习模型通过分析健康和营养数据,有效预测心血管疾病 (CVD) 死亡率. 关键的危险因素包括年龄,血压和食物元素,如纤维和.
科学领域:
- 心脏病学 心脏病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因.
- 确定可修改的风险因素对于有效的预防策略至关重要.
研究的目的:
- 为了确定心血管疾病 (CVD) 死亡率的风险因素.
- 用健康和饮食数据评估机器学习 (ML) 模型的预测性能.
主要方法:
- 使用了来自国家健康和营养检查调查 (NHANES) 的数据.
- 开发并比较了三个ML模型:仅饮食数据,仅非饮食健康数据和组合数据.
- 采用随机森林算法和沙普利增量解释 (SHAP) 值进行分析.
主要成果:
- 机器学习模型,特别是随机森林,在所有数据类别中显示出强大的预测一致性.
- 关键的健康预测因素包括年龄和缩血压.
- 确定了重要的营养预测因素是纤维,和维生素E.
结论:
- 全面的健康和饮食评估对于预测心血管疾病死亡率至关重要.
- 整合营养数据可以提高ML模型的CVD风险预测性能.
- 建议使用更大的数据集和详细的饮食回忆进行进一步的研究.
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