Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

Abstract

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.

An intelligent healthcare system for predicting and preventing dengue virus infection

“…Among them, speed of diagnosing the infected individuals, tracking their movement and tracing of their contacted persons, and depicting the risk level of infection at different locations, have remained the focus of epidemic controlling strategy. In this regard, Majumdar et al [25] had developed a fog-based approach to diagnose Kyasanur Forest Disease (KFD) using extremal optimization tuned neural network (EO-NN) at the fog devices and GPS-based visualization of each infected user and risk prone region on Google Maps. However, the authors haven't represented the epidemic perspective of the infection through maps.…”

Use of artificial intelligence in infectious diseases

“…1). Epidemiological studies enable us to predict an epidemic from very small foyer as shown in a recent work on Kyasanur forest disease which is a tick-borne viral infectious disease (Majumdar et al, 2018). Using extremal optimization tuned neural network, the team of scientists showed the high prediction rate and proposed localization data to be implemented in future databases in order to better control transmission.…”

An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak

“…Punj et al [ 21 ] presented a small overview of IoT functionality and its affiliation with the sensing and wireless techniques to implement the health care products in WBAN. Various machine learning algorithms for real-time health diagnosis application in WBAN were presented by [ 22 , 23 ]. In [ 24 ], the authors gave a brief overview of 3D model wearable network.…”