使用机器学习方法来诊断手腕道综合征
Erol Öten1, Nilüfer Aygün Bilecik2, Levent Uğur3
1Department of Physical Therapy and Rehabilitation, Faculty of Medicine, Amasya University, Amasya, Turkey.
Computer methods in biomechanics and biomedical engineering
|October 28, 2024
概括
机器学习准确地使用电肌图 (EMG) 数据对手综合征 (CTS) 严重程度进行分类. 具有ANOVA特征选择的决策树 (DT) 提供了最佳的分类性能.
科学领域:
- 神经学 神经学
- 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:
- 带道综合征 (CTS) 是一种流行的神经疾病.
- 目前的诊断依赖于体检和电肌图 (EMG).
- 对CTS严重程度的客观分类可以改善患者管理.
研究的目的:
- 为了分类手掌道综合征 (CTS) 的严重程度.
- 用EMG数据评估用于CTS分类的机器学习模型.
- 为了确定CTS分类的最佳特征选择方法.
主要方法:
- 利用了154名患者的EMG数据,包括运动/感官延迟,速度和振幅.
- 构建了一个六维的特征空间.
- 应用了各种机器学习分类器 (DT,LDA,NB,SVM,k-NN,ANN).
- 通过使用ANOVA,MRMR,Relieff和PCA来减少功能空间.
主要成果:
- 决策树 (DT) 分类器表现出卓越的性能.
- 与DT相结合的ANOVA特征选择产生了最好的结果.
- 在完整的和减少的特征空间中都实现了有效的分类.
结论:
- 机器学习,特别是带有ANOVA特征选择的DT,为分类CTS严重程度提供了可靠的方法.
- 这种方法可以提高CTS的诊断准确性和治疗规划.
- 结合ML的EMG数据为评估神经系统状况提供了一个强大的工具.
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