声母细分对慢性阻塞性肺病机器学习分类的影响.
Alper Idrisoglu1, Ana Luiza Dallora Moraes2, Abbas Cheddad2,3
1Department of Health, Blekinge Institute of Technology, 371 41, Karlskrona, Sweden. alper.idrisoglu@bth.se.
Scientific reports
|March 23, 2025
概括
在语音分析中对元音的细分显著改善了使用机器学习模型对慢性阻塞性肺病 (COPD) 的分类. 在CatBoost模型中,母音"a"的第二段显示了最高的准确性.
科学领域:
- 语音科学 语言科学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 慢性阻塞性肺病 (COPD) 分类的非侵入性方法对于患者管理至关重要.
- 基于母音的语音分析显示,对评估COPD的声器功能有希望.
- 机器学习 (ML) 模型需要广泛的数据集,需要数据增强技术,如细分.
研究的目的:
- 为了评估分段元音"a"发音对ML分类器 (CatBoost,Random Forest,SVM) 对COPD分类的性能的影响.
- 为了比较从母音发音中获得的全序列,分段智能和组智能数据集构建的有效性.
主要方法:
- 在三个数据集类型上训练了CatBoost,随机森林和支持矢量机器模型:全序列,分段智能和组智能.
- 使用嵌套交叉验证 (nCV) 方法与网格搜索进行超参数优化,以防止过拟合.
- 分析了性能指标,包括准确性,真正率 (TPR) 和真负率 (TNR).
主要成果:
- 当分析分段时,元音"a"发言的第二段超越了ML分类的完整序列数据集.
- 使用第二段,CatBoost模型实现了最高的准确性 (97.8%的验证,84.6%的测试).
- 带有第二部分的CatBoost模型证明了TPR和TNR的最佳平衡,表明了临床效用.
结论:
- 声母生成的时间敏感特征对于准确的COPD分类至关重要.
- 声母发音细分可以有效地捕捉这些时间属性,提高ML模型的性能.
- 需要对更大,更多样化的数据集进行进一步的研究,以提高概括性.
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