基于声信号的帕金森病的诊断,使用SHAP和硬投票组合方法
Paria Ghaheri1, Hamid Nasiri2, Ahmadreza Shateri1
1Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.
Computer methods in biomechanics and biomedical engineering
|September 29, 2023
概括
使用语音分析和机器学习的新方法准确诊断了帕金森病 (PD). 这种方法利用沙普利增量解释 (SHAP) 识别了用于早期PD检测的关键声乐生物标志物.
科学领域:
- 神经学 神经学
- 机器学习 机器学习
- 生物医学信号处理
背景情况:
- 帕金森病 (PD) 是一种普遍存在的渐进性神经系统疾病.
- 目前的诊断方法 (运动症状,神经成像) 往往不准确,昂贵,难以获得.
- 许多机器学习模型的"黑子"性质阻碍了诊断解释.
研究的目的:
- 开发一种新,准确和可解释的方法,用于使用语音信号早期诊断帕金森病.
- 采用沙普利增量解释 (SHAP) 来实现模型解释性和关键诊断特征的识别.
- 改进现有的帕金森病诊断技术.
主要方法:
- 利用语音信号和声学特征来诊断帕金森病.
- 应用皮尔森相关系数用于特征选择.
- 采用了极端梯度提升,轻梯度提升机,梯度提升和包装分类器.
- 整合了硬投票组合方法与SHAP进行特征重要性分析.
主要成果:
- 在诊断帕金森病时达到85.42%的准确性.
- 与最先进的方法相比,拟议的整体方法显示出更高的性能.
- SHAP分析成功地确定了PD诊断中重要的关键声乐特征.
结论:
- 这种新的基于语音的诊断方法为早期发现帕金森病提供了一种有希望,准确和可解释的方法.
- 这种技术可以作为一个有价值的工具,帮助临床医生诊断帕金森病.
- 这项研究强调了可解释机器学习在神经疾病诊断中的潜力.
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