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Classification of lung sounds using scalogram representation of sound segments and convolutional neural network.

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使用机器学习模型和数据预处理技术,对去离子化水中的葡萄糖水平进行分类.

Tri Ngo Quang1,2, Tung Nguyen Thanh1,3, Duc Le Anh1

  • 1International School, Vietnam National University, Hanoi, Vietnam.

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科学领域:

  • 生物医学光学 生物医学光学
  • 频谱学是一种光谱学.
  • 医疗保健中的机器学习

背景情况:

  • 精确的葡萄糖监测对于糖尿病管理至关重要.
  • 目前的方法是侵入性的,并引起不适.
  • 需要使用非侵入性技术来改善患者的服从性和治疗.

研究的目的:

  • 调查拉曼光谱法用于非侵入性葡萄糖度估计.
  • 应用机器学习模型来使用光谱数据对葡萄糖水平进行分类.
  • 评估数据预处理对模型准确性的影响.

主要方法:

  • 使用已知葡萄糖度的脱离离子化水样.
  • 采用拉曼光谱来收集光谱数据.
  • 应用机器学习模型:额外的树木,随机森林和支持向量机 (SVM).
  • 实施的数据预处理:光背景去除和热点系列提取.

主要成果:

  • 数据预处理技术显著提高了分类准确性.
  • 额外树木模型达到95%的最高准确度.
  • 拉曼光谱与机器学习相结合,显示出对估计葡萄糖水平的有希望.

结论:

  • 拉曼光谱是一种可行的非侵入性方法,用于血糖监测.
  • 机器学习模型,特别是额外的树木,可以准确地分类葡萄糖水平.
  • 数据预处理对于优化这些模型的性能至关重要.