低密度脂蛋白胆固醇估计的比较评估:机器学习算法与各种方程对比
Esra Paydaş Hataysal1, Muslu Kazım Körez2, Fatih Yeşildal3
1Department of Biochemistry, Göztepe Prof. Dr. Süleyman Yalçın City Hospital, Istanbul, Turkey.
概括
机器学习算法显著优于预测低密度脂蛋白胆固醇 (LDL-C) 的传统公式,提供更准确的心血管风险评估. 这些先进的方法显示出更大的一致性和更少的偏差,即使 triglyceride 水平高.
科学领域:
- 生物化学 生物化学
- 医疗信息学 医疗信息学
- 心血管疾病研究研究
背景情况:
- 准确的低密度脂蛋白胆固醇 (LDL-C) 测量对于心血管风险评估至关重要.
- 弗里德瓦尔德公式经常提供不准确的LDL-C估计.
- 新的方程和机器学习 (ML) 算法已被开发来改善LDL-C预测.
研究的目的:
- 将各种公式和ML算法估计的LDL-C与直接测量的LDL-C (dLDL-C) 的准确性进行比较.
- 为了评估Friedewald的表现,扩展了Martin-Hopkins,Sampson,de Cordova和Vujovic公式,以及五个ML算法,用于预测LDL-C.
- 评估这些方法与DLDL-C.的偏差和一致性.
主要方法:
- 分析了来自ISLAB-2核心实验室的29504个样本.
- 在所有统计分析中使用R版本4.1.2.
- 对比弗里德沃尔德,扩展的马丁-霍普金斯,桑普森,德科尔多瓦,Vujovic公式和五个ML算法 (包括BRNN和RF) 与DLDL-C.
主要成果:
- 贝叶斯规范神经网络 (BRNN) 和随机森林 (RF) 算法显示与DLDL-C的相关性最高 (r=0.957).
- 与传统的LDL-C估计方程相比,所有ML算法都表现出较少的偏差和与DLDL-C的优异一致.
- 在整个测试数据集中,ML算法表现优于现有的公式.
结论:
- 机器学习算法在预测LDL-C方面比传统方程更有效.
- ML算法利用大数据集,可以准确预测LDL-C,即使甘油三水平很高.
- 这些发现突出了ML在通过增强的LDL-C估计来改善心血管风险分层的潜力.
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