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以机器学习为基础,构建了对sarcopenic肥胖的预测模型

Mengru Xu^1,2, Jia Liu^1,2, Song Hu^1,2

¹Department of Health Care/Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China.

Frontiers in public health

|July 14, 2025

概括

机器学习模型可以使用临床因素来预测老年人肉骨肥胖症 (SO). 随机森林模型,利用BMI,巴特尔指数,握力和小腿周长,显示出最好的预测性能.

科学领域:

老年学是指老年学的学科.
医疗信息学医疗信息学
公共卫生公共卫生

背景情况:

肥胖症 (SO) 在老年人群中越来越令人担忧,与增加的残疾和死亡率有关.
早期识别SO对于管理老年人健康风险至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测老年人肉骨肥胖症 (SO).
确定与SO相关的关键临床因素.

主要方法:

利用了386名参与者的数据,分为培训 (80%) 和测试 (20%) 集.
采用单变量和多变量逻辑回归来确定SO的独立预测因子.
开发并交叉验证了五种ML模型 (RF,NB,LightGBM,KNN,XGBoost) 来预测SO.

主要成果:

确定了BMI,巴特尔指数得分,握力和小腿周长作为SO的独立预测因素.
随机森林 (RF) 模型实现了最高的曲线下的面积 (AUC) 0.839,用于预测SO.
牛犊周长成为评估SO风险的一个重要因素.

结论:

基于射频模型,结合BMI,巴特尔指数得分,握力和小腿周长的预测模型,可以可靠地预测SO.
这种基于ML的方法在临床实践中为早期SO检测提供了一个有希望的工具.

关键词:

这就是为什么RF是RF.牛犊周长的周长是多少机器学习是机器学习.肥胖肥胖肥胖肥胖肥胖肥胖肥胖肥胖这种类型的麻症是sarcopenia.

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