Authors: Vishal Kumar Goar; Nagendra Singh Yadav; Chiranji Lal Chowdhary; Kumaresan P; Mohit Mittal
Addresses: Government Engineering College, Bikaner, Rajasthan 334001, India ' Government Engineering College, Bikaner, Rajasthan 334001, India ' School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014 Tamil Nadu, India ' School of Information Technology & Engg., Vellore Institute of Technology Vellore, India ' INRIA, Nord Europe, CRIStAl, Lille, France
Abstract: World Health Organization has declared COVID-19 a pandemic. Like many other epidemic outbreaks, the COVID-19 pandemic also faces significant challenges. The digital technology allowed healthcare professionals in identification and isolation to the source of the infection to prevent community transmission of the virus by remotely monitoring the COVID-19 infected patients. We proposed a prediction model using Orange Canvas Program by creating a local instance dataset of eight suspected individuals' measured body parameters. Furthermore, six machine learning classifiers such as KNN, DT, SVM, random forest, neural network and naive Bayes are implemented to train the model on the dataset and in predicting the COVID-19. The results show that the proposed machine learning model successfully detects COVID-19. The evaluation results show that the highest accuracy value is obtained with neural networks and SVM, however neural networks outperform in other statistical parameters besides the accuracy rate.
Keywords: internet of things; IoT; artificial intelligence; pandemic; wearable sensors; COVID-19; cloud interface; machine learning.
International Journal of Networking and Virtual Organisations, 2021 Vol.25 No.3/4, pp.232 - 251
Received: 09 Aug 2020
Accepted: 09 Jan 2021
Published online: 10 Jan 2022 *