通过按键数据检测帕金森病的发现
Bahar Demir1, Sezer Ulukaya2, Oğuzhan Erdem2
1Department of Computational Science, Trakya University, Edirne, Turkey.
Computer methods in biomechanics and biomedical engineering
|August 21, 2023
概括
分析键盘打字模式的机器学习模型可以准确检测帕金森病 (PD). 这项研究利用按键动态来识别PD症状,提供了一个潜在的非侵入性诊断工具.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 帕金森病 (PD) 是一种普遍的神经疾病,其特点是神经损伤和运动障碍,如震和勃拉迪基尼西亚.
- 目前的诊断方法可能具有侵入性或缺乏早期检测能力,突出显示了对新方法的需求.
研究的目的:
- 开发和评估基于机器学习 (ML) 的模型,用于使用键盘键盘动态检测帕金森病.
- 从原始按键数据中识别关键特征,有效区分患有PD的个体.
主要方法:
- 从14个子数据集的原始键盘键盘数据中提取了378个特征,按药物使用,疾病严重程度和性别分类.
- 使用支持矢量机器 (SVM),k-近邻 (kNN) 和随机森林 (RF) 算法开发和比较了ML模型.
- 使用的特征选择技术包括最小冗余最大相关性 (mRmR),RELIEF和顺序前选择 (SFS),以及随机森林 (RF).
- 创建组合模型:功能组合 (FE) 结合流行的功能和模型组合 (ME) 结合多个ML算法通过多数投票.
主要成果:
- 特征组合 (FE) 模型实现了高精度,在14个数据集中达到91.73%至100%.
- 模型组合 (ME) 模型表现出强的性能,精度从81.08%到100%不等.
结论:
- 按键动态与机器学习相结合,为早期发现帕金森病提供了一个有希望的,非侵入性的方法.
- 整体方法,特别是特征整体,显示出基于打字模式准确的PD诊断的巨大潜力.
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