基于雾的智能心血管疾病预测系统由修改后的门式循环单元提供动力
A Angel Nancy1, Dakshanamoorthy Ravindran1, Durai Raj Vincent2
1Department of Computer Science, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli 620002, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
一个新的雾辅助智能医疗保健系统使用深度学习改进了心血管疾病的诊断. 这种边缘-雾-云模型显著降低了延迟,并提高了对时间关键医疗应用的准确性.
科学领域:
- * 计算机科学,人工智能,医疗保健技术
- *专注于边缘计算,雾计算和云计算集成.
背景情况:
- * 云计算提供基本服务,但面临着诸如物联网 (IoT) 的延迟和带宽限制等局限性.
- *物联网设备的扩散产生了大量的数据,加剧了云-物联网交互挑战.
- *雾计算提供了一个去中心化层,通过将计算更接近数据源来缓解这些问题.
研究的目的:
- * 提出一种雾辅助的智能医疗保健系统,用于诊断心脏或心血管疾病.
- *利用边缘-雾-云模型来优化医疗保健中的数据处理和预测分析.
- * 解决时间关键的医疗保健应用程序中的延迟,延迟和安全漏洞.
主要方法:
- * 集成一个模糊的推断系统 (FIS) 用于数据预处理.
- * 运用了Gated Recurrent Unit (GRU),一种反复的神经网络的变体,用于预测分析.
- * 实现了层次化的边缘-雾-云架构,在雾层进行了大量的处理.
主要成果:
- * 在心血管疾病诊断方面达到99.125%的高分类准确率.
- * 与仅使用云计算的解决方案相比,在延迟,响应时间和动方面取得了优化结果.
- *验证了深度学习模型在医疗保健的雾计算范式中的有效性.
结论:
- * 拟议的雾辅助系统显著增强了智能医疗诊断.
- * 边缘-雾-云模型有效地解决了庞大的数据量和延迟问题.
- * 深度学习与雾计算相结合,为时间关键的医疗保健应用提供了近乎完美的结果.
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