Open Access

ARTICLE

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

Mahmood Hussain Mir^1,*, Sanjay Jamwal¹, Ummer Iqbal², Abolfazl Mehbodniya³, Julian Webber³, Umar Hafiz Khan⁴

1 Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu & Kashmir, India
2 Department of Computer Sciences, National Institute of Electronics and Information Technology, Srinagar, Jammu & Kashmir, India
3 Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City, 20185145, Kuwait
4 Department of Geriatric Medicine, Sher-i-Kashmir Institute of Medical Sciences, Srinagar, Jammu & Kashmir, India

* Corresponding Author: Mahmood Hussain Mir. Email:

(This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)

Computer Modeling in Engineering & Sciences 2023, 137(3), 2529-2565. https://doi.org/10.32604/cmes.2023.027173

Received 17 October 2022; Accepted 23 March 2023; Issue published 03 August 2023

Abstract

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency, short response time, and optimal energy consumption. In this paper, the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models. The proposed models were validated using kcross-validation to ensure the consistency of models. Based on the experimental results, our proposed models have recorded good accuracies with highest of 97.767% by Support Vector Machine. According to the findings of experiments, the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects, as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

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Keywords

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