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在韩国单人家庭中识别代谢综合症的风险组和影响代谢综合症的因素,使用隐性类分析和机器学习技术:二次分析研究研究

Ji-Soo Lee¹, Soo-Kyoung Lee²

¹Department of Nursing, Gimcheon University, Gimcheon-si, Republic of Korea.

JMIR formative research

|September 12, 2023

概括

韩国单人家庭的代谢综合征风险使用机器学习进行了分析. 肥胖和腹部肥胖被确定为关键风险因素,特别是在中年人群中.

科学领域:

公共卫生公共卫生
流行病学流行病学
数据科学数据科学数据科学

背景情况:

韩国一个人家庭的增加与代谢综合征发病率的增加有关.
生活方式的改变有助于代谢综合征,这是心血管和脑血管疾病的前体.
不同的单人家庭需要对代谢综合征风险因素进行量身定制的分析.

研究的目的:

在单人家庭中确定影响代谢综合征的因素.
使用机器学习技术来识别风险因素.
使用隐性类分析 (LCA) 分类代谢综合征风险组.

主要方法:

使用10年国家健康和营养检查调查数据 (2009-2018) 进行横截面研究.
分析了来自单人家庭的1371名参与者.
应用了机器学习算法 (逻辑回归,决策树,随机森林,极端梯度提升) 和LCA.

主要成果:

隐性类别分析将参与者分为4个不同的风险组.
机器学习确定了年龄,BMI,肥胖,主观的身体形象,酒精,吸烟和工作类型作为关键因素.
中年患有肥胖和腹部肥胖的人 (4组) 表现出最高的代谢综合征易感性 (OR 17.67).

结论:

机器学习和LCA有效地确定了单人家庭中的代谢综合征风险组和因素.
这些发现使得开发定制干预措施的发展成为可能,这些干预措施针对的是代际风险因素.
这项研究为预防这种人口中的代谢综合征和相关疾病提供了关键的见解.

关键词:

隐性类分析隐性类分析机器学习是机器学习.代谢综合征代谢综合征风险因素的风险因素是什么单人家庭单人家庭

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