慢性阻塞性肺病的自动分类在一个新的收集和评估的语音数据集
Alper Idrisoglu1, Ana Luiza Dallora1, Abbas Cheddad1
1Blekinge Institute of Technology, Valhallavägen 1, 371 41 Karlskrona, Sweden.
Artificial intelligence in medicine
|September 2, 2024
概括
机器学习模型可以使用母音"a"发音的语音特征来检测慢性阻塞性肺病 (COPD). 这种方法有助于早期检测COPD,即使有轻微症状,通过分析声学和Mel频率 Cepstrum 系数.
科学领域:
- 肺部医学 肺部医学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 慢性阻塞性肺病 (COPD) 影响全球数百万人,由于轻微的早期症状,经常被诊断不足.
- 慢性肺炎的系统性影响,如语音扭曲,可能为早期检测提供线索.
研究的目的:
- 通过使用机器学习 (ML) 来调查母音"a"发音中的语音特征是否可以预测COPD.
- 探索语音分析在早期COPD检测和诊断方面的潜力.
主要方法:
- 收集了48名参与者对元音"a"的录音.
- 提取了声学和Mel频率 Cepstrum 系数 (MFCC) 的特征.
- 通过使用嵌套的k-fold交叉验证来训练和评估Random Forest,Support Vector Machine和CatBoost ML模型.
主要成果:
- CatBoost 分类器在测试组中达到最高准确率 (78%),在验证组中达到97%.
- CatBoost表现出强的性能,曲线下的面积 (AUC) 为82%,平均精度 (AP) 为76%.
- 年龄,性别和声学/MFCC特征被确定为COPD分类的重要预测因素.
结论:
- "a"元音的语音录音包含使用CatBoost模型对COPD分类的预测信息.
- 声学和MFCC特征以及年龄和性别可以支持COPD诊断的临床决策.
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