物联网支持的健康监测系统使用骑手优化算法和联合过程估计算法.
J Prabin Jose1, G Jaffino2, Mohammed Al Awadh3,4
1School of Electronics Engineering, Vellore Institute of Technology , Vellore, India. prabinjose@gmail.com.
Scientific reports
|July 29, 2025
概括
这项研究引入了一种使用MAX 30102和LM35传感器的新型实时健康监测系统. 拟议的联合过程估计器骑手优化算法 (JPEROA) 显著提高了健康状况检测的准确性,灵敏度和特异性.
科学领域:
- 生物医学工程 生物医学工程
- 传感器技术 传感器技术
- 机器学习用于医疗保健
背景情况:
- 及时检测异常健康状况对于有效的医疗干预和改善患者结果至关重要.
- 现有的实时健康监测系统在实现高精度,灵敏度和特异性方面经常面临局限性.
- 需要在实时传感器数据分析中提高性能,用于健康监测.
研究的目的:
- 开发一个改进的实时健康监测系统,利用传感器数据.
- 为健康监测中的分类任务提出和评估联合流程估计器骑手优化算法 (JPEROA).
- 将拟议的JPEROA方法的性能与已建立的机器学习算法进行比较.
主要方法:
- 使用MAX 30102和LM35传感器收集的实时生理数据 (心率,血液氧气,体温).
- 通过ThingSpeak物联网 (IoT) 云平台进行数据传输和分析.
- 传感特征的标准化,以实现统一的缩放.
- 实施联合流程估计器骑手优化算法 (JPEROA) 与深自动编码器进行分类,估计线和延迟系数.
- 使用支向量机,随机森林,梯度增强,天真贝斯和多层感知器进行比较分析.
- 使用PTB诊断数据集进行验证.
主要成果:
- 拟议的JPEROA方法实现了0.9625.5的最大精度.
- 该系统的最大灵敏度为0.975,特异性为0.95.
- 在精度,灵敏度和特异性方面,JPEROA在其他评估的机器学习算法中表现优于其他算法.
结论:
- 与Deep Stack自动编码器集成的JPEROA算法为实时健康监测提供了显著的进步.
- 拟议的系统有效地提高了检测异常健康状况的准确性,敏感性和特异性.
- 这种方法有望通过更可靠,更精确的健康监测来改善患者的治疗结果.
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