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

Abhishek Majumdar1, Tapas Debnath2, Sandeep K Sood3

  • 1Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India. abhishek.nits@ieee.org.

Journal of Medical Systems
|September 3, 2018
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Summary

Kyasanur Forest Disease (KFD), a tick-borne illness, can now be monitored early using a new fog computing e-Healthcare system. This system employs an Extremal Optimization tuned Neural Network (EO-NN) for accurate KFD detection and outbreak prevention.

Keywords:
Extremal optimizationFog computingNeural networke-Healthcare

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Area of Science:

  • Medical Informatics
  • Epidemiology
  • Computer Science

Background:

  • Kyasanur Forest Disease (KFD) is a severe tick-borne viral illness endemic to South Asia.
  • KFD has recently expanded its reach, posing a significant epidemic risk.
  • Early detection and monitoring are crucial for managing KFD outbreaks.

Purpose of the Study:

  • To propose a novel fog computing-based e-Healthcare framework for early KFD patient monitoring.
  • To develop an advanced classification algorithm for high prediction rates of KFD infection.
  • To implement a location-based alert system for timely KFD outbreak prevention.

Main Methods:

  • Development of a hybrid Extremal Optimization tuned Neural Network (EO-NN) classification algorithm.
  • Integration of fog computing for real-time e-Healthcare monitoring.
  • Implementation of a Global Positioning System (GPS)-based alert system for infected users and risk zones.

Main Results:

  • The proposed EO-NN algorithm achieved an average accuracy of 91.56%.
  • EO-NN demonstrated high sensitivity (91.53%) and specificity (97.13%) in classifying KFD cases.
  • Comparative analysis confirmed EO-NN's superior performance over existing classification methods.

Conclusions:

  • The developed fog computing e-Healthcare framework effectively supports early KFD monitoring.
  • The EO-NN algorithm provides accurate classification and identification of KFD risk areas.
  • The proposed system offers a promising approach for KFD outbreak prevention and control.