应用多种机器学习模型来对社区居住的老年人语音轻度认知障碍进行分类
Renqing Zhao1, Zhiyuan Zhu1, Zihui Huang1
1College of Physical Education, Yangzhou University, Yangzhou 225009, China.
Journal of Intelligence
|February 26, 2026
概括
使用语音特征的机器学习模型有效地识别早期轻度认知障碍 (MCI). 优化语音分类和机器学习显著提高了区分MCI患者与健康对照患者的准确性.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 认知障碍,特别是轻度认知障碍 (MCI),对早期发现构成了重大挑战.
- 开发可访问和准确的查工具对于及时干预和认知衰退的管理至关重要.
研究的目的:
- 开发和评估用于早期检测轻度认知障碍 (MCI) 的机器学习模型,使用语音特征.
- 将优化的语音分类功能与机器学习算法集成在一起,以提高诊断准确度.
主要方法:
- 在图片描述任务中从65名MCI患者和55名健康对照 (HCs) 收集了音频数据.
- 使用Librosa库提取41个语音分类特征.
- 在随机森林 (RF) 和支持向量机 (SVM) 模型中应用顺序前向选择 (SFS) 进行特征优化.
- 在预处理的语音数据上训练了一个极端梯度增强 (XGBoost) 模型.
主要成果:
- 优化的SVM模型实现了0.825的精度 (AUC:0.91).
- 优化的射频模型达到0.88的精度 (AUC:0.86).
- XGBoost模型的精度为0.92 (AUC:0.91),表现出卓越的性能.
结论:
- 基于语音的机器学习模型显著提高了区分MCI患者与健康老年人的准确性.
- 这些发现支持了语音分析与机器学习相结合的潜力,作为早期认知缺陷识别的可靠工具.
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