从咳记录中检测结核病,使用袋词分类器
Irina Pavel1, Iulian B Ciocoiu1
1Faculty of Electronics, Telecommunications and Information Technology, Gheorghe Asachi Technical University of Iasi, Bd. Carol I 11A, 700050 Iasi, Romania.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了Bag-of-Words分类器,用于从咳声音中检测结核病. 该方法显示了有希望的准确性和曲线下面积 (AUC) 值,提供了可靠的诊断工具.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 呼吸系统医学 呼吸系统医学
背景情况:
- 结核病 (TB) 检测仍然是一个全球卫生挑战,需要创新的诊断方法.
- 咳记录为潜在的结核病识别提供了一个非侵入性和可访问的数据源.
研究的目的:
- 通过使用咳记录来评估Bag-of-Words (BoW) 分类器对可靠结核病检测的有效性.
- 评估不同特征提取和编码策略对分类器性能的影响.
主要方法:
- 使用 Bag-of-Words 分类器,具有独特的特征提取和编码策略.
- 在两个大,独立的数据集上进行了实验,包括增强记录.
- 使用重复的k倍交叉验证和外部数据集进行绩效评估.
主要成果:
- 达到0.77的精度和0.84的曲线下面积 (AUC).
- 与现有的结核病检测方法相比,表现良好.
- 在各种参数组合和数据集增强中展示了稳定性.
结论:
- 词袋分类器提供了一种可靠和有效的方法,通过咳声音检测结核病.
- 拟议的方法为增强结核病诊断提供了一个有希望的,非侵入性的工具.
- 进一步的研究可以探索先进的功能工程和深度学习集成.
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