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逻辑回归和统计规范化技术用于冠状动脉疾病的风险分类,使用由高密度脂蛋白运输的细胞因子

Seema Singh Saharan¹, Pankaj Nagar², Kate Townsend Creasy³

¹Department of Clinical Pharmacy, University of California, San Francisco, USA, UCSF Kane Lab, San Francisco, USA, UC Berkeley Extension, Berkeley, USA.

Proceedings. International Conference on Computational Science and Computational Intelligence

|November 1, 2024

概括

机器学习模型通过分析年龄和细胞因子生物标志物,准确地识别出患冠状动脉疾病 (CAD) 风险的个体. 脊柱回归实现了最高的准确性,突出了改善CAD风险评估的关键炎症标志物.

科学领域:

心血管医学心血管医学
生物标志物和炎症的研究
计算生物学和机器学习

背景情况:

冠状动脉疾病 (CAD) 仍然是全球首要的死亡原因.
动脉样硬化,是CAD的根本原因,是一种涉及细胞因子的炎症过程.
新型生物标志物,如在高密度脂蛋白 (HDL) 上运输的细胞因子,为主动风险评估提供了潜力.

研究的目的:

实施机器学习算法来识别患有CAD风险的个体.
通过使用年龄和多维细胞因子生物标志物来评估后勤回归,LASSO和回归的有效性.
探索HDL相关细胞因子在血管炎症和CAD风险预测中的作用.

主要方法:

使用了逻辑回归和规范化技术 (LASSO,回归) 与特征选择.
采用k-fold交叉验证和超参数调整来优化模型.
使用接收器操作特征 (AUROC) 曲线下的面积来评估模型性能.

主要成果:

随着特征选择的回归实现了最高的AUROC得分0.878 (95% CI:0.837,0.92).
拉索回归产生了0.875的AUROC (95%CI:0.832,0.917),而后勤回归则达到0.85 (95%CI:0.804,0.897).
通过回归确定的主要生物标志物包括年龄,IL-7,RANTES,IFN-和IL-3.

结论:

机器学习,特别是回归,有效地使用细胞因子配置文件识别CAD风险.
高脂相关的细胞因子是血管炎症和CAD风险的重要指标.
这些发现推动了个人化医疗方法用于CAD评估和治疗.

关键词:

在这里,我们可以看到CAD,CAD,CAD.细胞因子 (cytokines) 是一种细胞因子.拉索回归拉索回归后勤回归的逻辑回归脊柱回归是一种回归.

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