ANN Assisted-IoT Enabled COVID-19 Patient Monitoring

Geetanjali Rathee, Sahil Garg, Georges Kaddoum, Yulei Wu, Dushantha Nalin K. Jayakody, Atif Alamri

Research output: Contribution to journalArticlepeer-review

Abstract

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients’ health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Artificial intelligence
  • Artificial neural network
  • Back propagation network
  • COVID 19 patients’ identification
  • COVID-19
  • Diseases
  • Medical services
  • multi-perceptron layer
  • Pandemics
  • security in healthcare
  • Viruses (medical)
  • Viterbi algorithm

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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