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

• Published in: Journal of medical systems Vol. 42; no. 10; pp. 187 - 16
• Main Authors: Majumdar, Abhishek, Debnath, Tapas, Sood, Sandeep K., Baishnab, Krishna Lal
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-10-2018; Springer Nature B.V
• Subjects: Algorithms; Bayes Theorem; Classification; Cloud computing; Comparative studies; Disease control; Disease Outbreaks; Epidemics; Global positioning systems; GPS; Health care; Health Informatics; Health risks; Health Sciences; Humans; Hybridization; Infectious diseases; Information systems; Kyasanur Forest Disease - diagnosis; Medicine; Medicine & Public Health; Mobile & Wireless Health; Neural networks; Neural Networks, Computer; Optimization; Outbreaks; Pest outbreaks; Plant diseases; Satellite navigation systems; Statistics for Life Sciences; Fog computing; e-Healthcare; Neural network; Extremal optimization
• DOI: 10.1007/s10916-018-1041-3
• EISSN: 1573-689X
• ISSN: 0148-5598

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.