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使用NIR技术与机器学习进行非侵入性葡萄糖预测和分类.

M Naresh1, V Siva Nagaraju2, Sreedhar Kollem3

  • 1School of Electronics Engineering, VIT-AP University, Amaravti, Guntur, 522241, Andhra Pradesh, India.

Heliyon
|April 11, 2024
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概括

这项研究引入了一种双波长近红外 (NIR) 系统,用于准确的,非侵入性的血糖监测. 该系统实现了高精度,与传统方法相美,为糖尿病管理提供了具有成本效益的解决方案.

关键词:
吸收能力 吸收能力分类 分类 分类 分类.探测器 探测器 探测器葡萄糖是一种葡萄糖.在红外线下,红外线是红外线.机器学习 机器学习这是一种非侵袭性的方法.回归是一种回归.频谱学是一种光谱学.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗器械 医疗器械
  • 光学传感传感器是什么?

背景情况:

  • 精确的血糖监测对于糖尿病管理至关重要.
  • 目前的方法,如指针测试是侵入性的,可能是不方便的.
  • 非侵入性葡萄糖检测方法为改善患者遵守提供了有希望的替代方案.

研究的目的:

  • 开发和评估双波长短近红外 (NIR) 系统,用于准确,非侵入性地检测葡萄糖水平.
  • 为了评估系统的性能与参考指针针葡萄糖装置相比.
  • 用先进的机器学习算法来预测和分类葡萄糖水平.

主要方法:

  • 设计和实施了一种双波长短NIR光谱系统.
  • 实时采集和分析血糖样本.
  • 用一个前神经网络 (FFNN) 回归模型来预测葡萄糖水平.
  • 为了评估临床准确性,进行了监测错误网格 (SEG) 分析.
  • 多层感知器 (MLP) 和K-最近邻居 (KNN) 分类器用于葡萄糖水平分类.

主要成果:

  • 该FFNN回归模型显示了0.99.9的高确定系数 (R2).
  • 关键的误差指标包括平均绝对误差 (MAE) 为2.49 mg/dl,根平均平方误差 (RMSE) 为3.02 mg/dl,平均绝对百分比误差 (MAPE) 为1.94%,平均平方误差 (MSE) 为9.16.
  • SEG分析证实,该系统的测量值在临床上可接受的范围内.
  • MLP和KNN分类器在分类葡萄糖水平方面实现了99%的准确性.

结论:

  • 开发的双波长短NIR系统为血糖监测提供了一个高度准确和非侵入性的方法.
  • 该系统的性能通过FFNN回归和分类验证,与传统的侵入性方法相美.
  • 这项技术为持续的葡萄糖监测提供了具有成本效益和方便的解决方案,有可能改善糖尿病护理.