Now-a-days computational offloading and quick disease prediction are identified as major difficult troubles of smart healthcare systems. As cloud based healthcare is primarily far away from remote patients which leads to high latency, bandwidth and computational usage at some point in real-time monitoring and diagnosis. Due to the expanded demand of real-time healthcare applications, there’s a want for higher quality of experience to carry out the disease prediction undertaking via low latency computational processing. So, the proposed research studies introduces an edge-cloud enabled healthcare system to carry out quick disease prediction in peer-to-peer edge-cloud integrated computing platform by minimizing the latency, bandwidth and computational power metrics. Consequently, a brand new fashion of adaptive cost and energy aware computational offloading scheme and privacy preserving communication protocol using hybrid encryption scheme are introduced. It will perform the real-time secure data offloading and processing among the edge nodes from the region where the affected patient records were initially collected. Also, an Adaptive Fuzzy Optimized k-Nearest Neighbor (AFO-k-NN) classifier model is introduced to predict Parkinson disease and primarily based on severity further diagnosis and rehabilitation process might be achieved by the healthcare system. Experimental evaluation on various offloading procedures and classifier models are made with respect to energy consumption, utility cost, response time, prediction time and prediction accuracy. Our expertise best, the proposed offloading scheme takes very less energy consumption, utility cost and response time while comparing to current cost-based, energy-based and learning-based schemes. In addition, the proposed privacy preserving communication protocol provides more privacy and security during offloading and sharing of patient data. Similarly, the proposed classifier model obtains less prediction time and greater prediction accuracy at the same time as compared to current classifier models.

如今，计算负荷和疾病快速预测被认为是智能医疗系统的主要难题。由于基于云的医疗保健主要远离远程患者，因此在实时监控和诊断的某些时候会导致高延迟，带宽和计算使用率。由于实时医疗保健应用的需求不断增长，因此需要更高的体验质量，以通过低延迟的计算处理来进行疾病预测。因此，拟议的研究引入了一种启用边缘云的医疗保健系统，以通过最小化等待时间，带宽和计算能力指标，在对等边缘云集成计算平台中进行疾病快速预测。最后，介绍了一种新型的自适应成本和能量感知计算分流方案以及使用混合加密方案的隐私保护通信协议。它将在最初收集受影响患者记录的区域的边缘节点之间执行实时安全数据卸载和处理。此外，引入了自适应模糊优化的k最近邻（AFO-k-NN）分类器模型来预测帕金森氏病，并且主要基于严重程度，医疗保健系统可能会实现进一步的诊断和康复过程。针对能耗，公用事业成本，响应时间，预测时间和预测精度，对各种卸载程序和分类器模型进行了实验评估。我们最好的专业知识，与目前的基于成本，基于能源和基于学习的方案相比，所提出的卸载方案所需的能源消耗，公用事业成本和响应时间少得多。另外，提出的隐私保护通信协议在卸载和共享患者数据期间提供了更多的隐私和安全性。类似地，与当前分类器模型相比，所提出的分类器模型同时获得更少的预测时间和更高的预测精度。