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使用监督机器学习预测代谢综合征:一种多变量参数方法

Rodolfo Iván Valdez Vega¹, Jacqueline Alejandra Noboa-Velástegui^1,2, Ana Lilia Fletes-Rayas³

¹Programa de Doctorado en Ciencias Biomédicas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara C.P. 44340, Jalisco, Mexico.

International journal of molecular sciences

|October 29, 2025

概括

机器学习模型有效地使用adipokines和风险因素预测代谢综合征 (MetS). 关键指标包括年龄,人体指数,胰岛素耐药性,脂质概况和阿迪波内克水平,用于早期检测.

科学领域:

生物统计学生物统计学
计算生物学计算生物学
内分泌学在内分泌学.

背景情况:

代谢综合征 (MetS) 是一个日益增长的全球健康挑战.
现有的MetS预测标记因其越来越普遍而需要增强.

研究的目的:

开发和评估用于预测MetS的机器学习模型.
整合阿迪波金,代谢,心血管风险因素和人类指数,以改善预测.

主要方法:

利用了来自墨西哥瓜达拉哈拉的381名受试者 (20-59岁) 的数据.
开发并比较了四种监督机器学习模型:物流回归 (LR),支持矢量机器 (SVM),随机森林 (RF) 和极端梯度提升 (XGBoost).
使用AUC,校准曲线和决策曲线分析 (DCA) 评估模型性能.

主要成果:

射频和XGBoost模型表现出优异的预测性能,AUC分别为0.940和0.954.
射频和射频传输模型在DCA中表现出最佳校准和最高的净效益.
确定了关键的预测变量:年龄,人体指数 (BRI,DAI),HOMA-IR,sdLDL-C,LDL-C和高分子量腺素.

结论:

机器学习模型,特别是RF和XGBoost,显示出对MetS预测的巨大潜力.
人类测量变量,心血管风险因素,新陈代谢概况和脂肪素是MetS的关键指标.
这项研究强调了综合数据和先进建模的实用性,用于识别风险较高的MetS个体.

关键词:

车身圆度指数 (Body Roundness Index) 是一个指标.具有高分子量脂蛋白的脂蛋白.机器学习是机器学习.代谢综合征代谢综合征 sdLDL-C 是一个字体.

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