ANN Assisted-IoT Enabled COVID-19 Patient Monitoring

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

doi:10.1109/ACCESS.2021.3064826

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring

IEEE Access. 2021 Mar 9:9:42483-42492. doi: 10.1109/ACCESS.2021.3064826. eCollection 2021.

Authors

Geetanjali Rathee¹, Sahil Garg^{2

3}, Georges Kaddoum², Yulei Wu⁴, Dushantha Nalin K Jayakody^{3

5}, Atif Alamri⁶

Affiliations

¹ Department of Computer Science and EngineeringJaypee University of Information Technology Waknaghat 173234 India.
² Electrical Engineering DepartmentÉcole de Technologie SupérieureUniversité du Québec Montréal QC H3C 1K3 Canada.
³ School of Computer Science and RoboticsNational Research Tomsk Polytechnic University (TPU) 634034 Tomsk Russia.
⁴ College of Engineering, Mathematics and Physical SciencesUniversity of Exeter Exeter EX4 4QF U.K.
⁵ Centre for Telecommunication Research, Faculty of Engineering & TechnologySri Lanka Technological Campus Padukka 11500 Sri Lanka.
⁶ Chair of Pervasive and Mobile Computing, College of Computer and Information SciencesKing Saud University Riyadh 11543 Saudi Arabia.

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.

Keywords: Artificial neural network; COVID 19 patients’ identification; back propagation network; multi-perceptron layer; security in healthcare.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Grants and funding

This work was supported in part by the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia through the Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing, in part by the Tomsk Polytechnic University, Russia, through the Competitive Enhancement Program, and in part by the Sri Lanka Technological Campus, Sri Lanka, Seed under Grant RRSG/20/B15.