机器学习算法的比较分析用于将aptamer-antigen结合动态配置文件转化为诊断决策
Sadman Sakib1, Kulmanak Bajaj2, Payel Sen1
1Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
ACS sensors
|January 27, 2025
概括
机器学习 (ML) 显著改善了用于诊断的生物传感器数据分类. 这一框架提高了使用人工神经网络 (ANN) 和随机森林 (RF) 的COVID-19检测准确度,超出了传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习应用 机器学习应用
- 生物传感器技术技术
背景情况:
- 传统的生物传感器数据分类使用固定值 (ROC曲线),限制了复杂信号的准确性.
- 诊断中的实时电化学测量需要先进的分析方法.
- 现有的方法在高变量生物传感器数据上扎,影响诊断可靠性.
研究的目的:
- 开发和验证一种机器学习 (ML) 框架,用于增强生物传感器诊断数据的二进制分类.
- 为了提高复杂和可变的电化学信号的诊断准确度.
- 将ML应用于实时多重体体测定 (RT-MAp) 进行病毒蛋白检测.
主要方法:
- 开发了一个框架,用于将ML应用于实时电化学生物传感器数据.
- 通过非线性回归,从172个COVID-19唾液样本的短暂阻抗信号中提取了9个关键特征.
- 监督的ML算法 (SVM,ANN,RF) 在提取的特征上进行训练和测试.
主要成果:
- 机器学习模型显著优于基于ROC的传统分类 (83.6%的准确率).
- 支持矢量机 (SVM) 实现了86.0%的准确性.
- 人工神经网络 (ANN) 和随机森林 (RF) 模型实现了100%的准确性.
- 通过高变量生物传感器数据,ANN表现出卓越的性能.
结论:
- 开发的ML框架为改善诊断准确性提供了强大而可扩展的解决方案.
- 从生物传感器数据中,ANN和RF模型提供了非常准确的临床样品二元分类.
- 这种方法提高了使用电化学生物传感器的临床诊断的可靠性.
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