基于高尔距离的新型语音分类,用于检测帕金森病
Mustafa Noaman Kadhim1, Dhiah Al-Shammary1, Fahim Sufi2
1College of Computer Science and Information Technology, University of Al-Qadisiyah, Dewaniyah, Iraq.
International journal of medical informatics
|August 3, 2024
概括
一个新的Gower距离分类器从语音数据中检测帕金森病 (PD) 的准确率达到98.3%. 这种新的方法优于传统方法,为早期PD诊断提供了可靠的工具.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 神经系统疾病 诊断 诊断 神经系统疾病 诊断
背景情况:
- 像SVM和Random Forest这样的传统分类器面临着高维度医疗数据的挑战.
- 从语音记录中准确检测帕金森病 (PD) 对于早期干预至关重要.
研究的目的:
- 为改善帕金森病 (PD) 检测引入一种基于Gower距离的新型分类器.
- 为了解决处理复杂的现有方法的局限性,用于PD诊断的高维语音数据集.
主要方法:
- 利用Gower距离度量来有效地测量各种语音录制特征之间的差异.
- 采用Cuckoo Search算法进行优化功能选择,减少数据维度和计算负载.
- 开发了一种新型分类器,集成了Gower距离和Cuckoo Search用于PD检测.
主要成果:
- 当与特征选择相结合时,拟议的Gower距离分类器实现了98.3%的高精度.
- 与PD检测传统方法和最近的研究相比,该分类器表现出优异的性能.
- 即使没有特征选择步骤,也记录了94.92%的准确性,突出显示了Gower距离计的稳定性.
结论:
- 基于距离的Gower分类器显示出作为帕金森病可靠诊断工具的巨大潜力.
- 该方法为PD检测提供了更高的准确性和效率,帮助医疗从业者.
- 这种方法可以提高PD诊断和监测在临床环境和远程老年护理.
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