Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India.
Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India.
J Med Syst. 2018 Sep 1;42(10):187. doi: 10.1007/s10916-018-1041-3.
Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.
基孔肯雅热（KFD）是一种危及生命的蜱传病毒性传染病，流行于南亚，在过去十年中每年夺走许多人的生命。但最近，这种疾病在其他地区也有很大程度的发生，并可能很快成为一种流行病。在本文中，提出了一种基于雾计算的新型电子医疗保健框架，用于在感染的早期阶段监测 KFD 感染患者并控制疾病爆发。为了确保高预测率，使用极值优化与前馈神经网络的混合，开发了一种新的极值优化调谐神经网络（EO-NN）分类算法。此外，还提出了一种基于位置的警报系统，以尽早提供每个 KFD 感染用户的全球定位系统（GPS）位置信息和高风险区域，以防止疫情爆发。此外，还对所提出的 EO-NN 与最先进的分类算法进行了比较研究，可以得出结论，EO-NN 在分类和准确识别高风险区域方面的平均准确率为 91.56%、灵敏度为 91.53%和特异性为 97.13%，表现优于其他算法。
