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机器学习方法用于生物标志物发现,以预测大动脉动脉样硬化

Ting-Hsuan Sun¹, Chia-Chun Wang¹, Ya-Lun Wu¹

¹Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan.

Scientific reports

|September 13, 2023

概括

这项研究开发了一种机器学习方法,使用临床因素和代谢物预测大动脉动脉样硬化 (LAA). 该模型实现了高精度,为LAA提供了具有成本效益的诊断工具.

科学领域:

生物医学信息学生物医学信息学
心血管研究研究心血管研究
代谢学代谢学代谢学

背景情况:

大动脉动脉样硬化 (LAA) 是脑血管疾病的主要原因,但其诊断具有挑战性和资源密集.
现有的LAA预测生物标志物显示不一致的结果.
代谢物越来越被认为是各种健康状况的潜在生物标志物.

研究的目的:

开发和验证一种基于机器学习的新方法来预测LAA.
为了确定可靠的临床和代谢生物标志物来预测LAA.
为了提高LAA诊断的效率和准确性.

主要方法:

集成多个机器学习算法与特征选择技术来分析多维数据.
利用后勤回归 (LR) 作为表现最好的预测模型.
采用递归特征消除和交叉验证来进行可靠的生物标记物识别.

主要成果:

后勤回归模型在外部验证中实现了0.92的AUC,具有62个特征.
关键预测因素包括体重指数,吸烟,糖尿病,高血压,高脂血症药物,以及氨基酸-tRNA生物合成和脂质代谢中的代谢物.
一套精细的27个共享特征改善了LR模型的AUC到0.93.

结论:

机器学习与特征选择相结合,有效地识别了LAA的生物标志物.
跨模型的共享功能提高了预测可靠性,对未来的LAA生物标志物发现有价值.
这种方法为LAA预测提供了一个有希望的,数据驱动的策略.

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