使用XGBoost模型与声乐生物标志物进行成人喘的非侵入性声学分类
Yi Lyu1,2, Quan-Cheng Jiang1, Shuai Yuan3
1School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People's Republic of China.
Scientific reports
|August 6, 2025
概括
机器学习准确地使用非侵入性语音分析对成人喘进行分类. 这种声学方法为早期诊断和管理提供了传统螺旋计的有希望的替代方案.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 传统的喘诊断,如螺旋计,由于患者的合作而面临局限性.
- 非侵入性声学分析为喘诊断提供了一个潜在的客观方法.
- 机器学习可以分析声乐特征以检测疾病.
研究的目的:
- 开发和验证一种机器学习模型,用于使用声学特征对成人喘进行分类.
- 评估各种机器学习模型对此诊断任务的性能.
- 为了确定喘的关键声学预测因素.
主要方法:
- 从成年人队伍中收集了 /ɑː/ 声音的语音录音 (初级n=214,外部n=200).
- 使用扩展的日内瓦极简主义声学参数集提取声学特征.
- 对比了七个机器学习模型,选择了极端梯度增强 (EGB) 进行进一步验证.
主要成果:
- 在试验组中,EGB模型实现了高精度 (0.8514) 和AUC (0.9130).
- 外部验证证明了持续的性能 (精度为0.8100,AUC为0.8755).
- 解释性分析强调了形式频率作为重要的预测因素.
结论:
- 使用声学特征的EGB模型是一种可行的,非侵入性的成人喘分类方法.
- 这种方法显示了早期诊断和远程喘管理中可访问的工具的潜力.
- 声学分析为现有的喘诊断策略提供了一个有希望的补充.
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