在基于CR的智能医疗保健系统中使用可优化的树机器学习方法进行频谱评估
Ahmad Raza1, Mohsin Ali1, Muhammad Khurram Ehsan2
1Department of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
可优化的树算法增强了认知无线电 (CR) 智能医疗保健系统中的频谱传感. 这种机器学习方法改善了实时患者数据传输,以更快地做出医疗决策.
科学领域:
- 无线通信无线通信
- 机器学习 机器学习
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 现代技术推动了对实时医疗监测的需求.
- 智能医疗保健系统利用传感器进行患者数据传输.
- 认知无线电 (CR) 为医疗保健提供频谱高效的数据传输.
研究的目的:
- 调查基于CR的智能医疗保健中用于频谱传感的基于树的算法 (TBA).
- 用模拟和理论数据集评估各种TBA的性能.
- 在这种情况下,确定用于频谱传感的最准确的TBA.
主要方法:
- 基于检测概率 (Pd) 和错误报警概率 (Pf) 的生成数据集.
- 训练并测试了多个TBA:精细树,粗树,整体增强树,中等树,整体包装树,整体RUSBoosted树和可优化的树.
- 计算每个TBA的训练和测试准确度,使用不同的信号样本.
主要成果:
- 可优化树在频谱传感方面表现出卓越的准确性.
- 可优化树实现了最小分类错误 (MCE).
- 在不同的TBA和数据集类型中比较了准确性.
结论:
- 可优化树对于基于CR的智能医疗保健中的频谱传感非常有效.
- 准确的频谱传感对于可靠的实时医疗数据传输至关重要.
- 机器学习,特别是TBA,为增强智能医疗保健系统提供了一个有前途的解决方案.
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