内脏脂肪代谢得分与喘发病风险之间的关联:基于NHANES数据库的机器学习分析
Chao Li1, Mingjun Ying, Fen Wu
1The First People's Hospital of Jiande, Hangzhou, China.
这项研究使用机器学习探索了内脏脂肪 (METS-VF) 与喘风险之间的联系. 虽然METS-VF显示了关联,但BMI是喘发病率的最强预测因素.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 内脏脂肪的积累与各种慢性疾病有关.
- 喘仍然是一个重要的公共卫生问题,具有复杂的风险因素.
- 预测模型可以提高对喘发病率的理解.
研究的目的:
- 调查内脏脂肪代谢得分 (METS-VF) 与喘发病风险之间的关联.
- 评估机器学习模型在预测喘风险方面的表现.
- 通过变量重要性分析,确定喘发病率的关键预测因素.
主要方法:
- 利用了来自全国健康和营养检查调查 (2001-2018) 的数据,其中有13,695名参与者.
- 采用Boruta算法用于变量选和CatBoost用于预测建模.
- 应用夏普利添加剂解释用于变量重要性评估.
主要成果:
- CatBoost模型实现了最高的预测准确性 (AUC = 0.640).
- 确定的主要预测因素包括体重指数,性别,种族,婚姻状况和吸烟史.
- 在METS-VF和喘发病风险之间发现了显著的关联.
结论:
- 机器学习,特别是CatBoost,提供了具有可解释性的有效喘风险预测.
- 体重指数是影响喘发病率的主要因素.
- 作为可能导致喘风险的潜在因素,METS-VF需要进一步调查.
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