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规范化机器学习模型用于预测代谢综合征使用GCKR,APOA5和BUD13基因变异:德黑兰心脏代谢遗传研究

Nadia Alipour¹, Anoshirvan Kazemnejad¹, Mahdi Akbarzadeh²

¹Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.

|August 29, 2023

概括

规范化的机器学习模型通过整合遗传和环境因素,准确地分类代谢综合征 (MetS). 这些先进的模型提供了改进的诊断能力,用于识别患有MetS高风险的个体.

科学领域:

遗传学和基因组学遗传学和基因组学
机器学习机器学习
公共卫生公共卫生

背景情况:

代谢综合征 (MetS) 由于其复杂,多因素的性质,对全球健康构成重大挑战.
识别高风险的个人 MetS 对于有效的预防和管理策略至关重要.

研究的目的:

使用规范化的机器学习模型对代谢综合征 (MetS) 进行分类.
评估遗传风险变体 (GCKR,BUD13,APOA5) 和环境因素在MetS分类中的预测能力.
为了比较各种规范化技术与经典物流回归的性能.

主要方法:

一项包括2,346例病例和2,203例对照在内的队列研究,来自德黑兰心脏代谢遗传研究 (TCGS).
应用规范化方法,包括LASSO,回归,弹性网,自适应LASSO和自适应弹性网.
使用10次重复的10倍交叉验证与准确性,AUC-ROC和AUC-PR等指标进行评估.

主要成果:

在随访期间,超过50%的参与者出现了MetS.
MetS与年龄,性别,BMI,学历和特定的遗传风险变异有显著的关联.
与逻辑回归相比,规范化的机器学习模型表现出更高的性能,适应式LASSO是最节的.

结论:

规范化的机器学习模型为MetS分类提供了更高的准确性和节性.
这些模型可以作为临床决策支持工具的基础.
整合遗传和人口统计数据有助于识别患有MetS高风险的个人.

关键词:

分类分类分类分类.拉索·拉索 (Lasso) 是一个机器学习机器学习代谢综合征是代谢综合征.受到惩罚的回归.

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