@Article{cmes.2023.027173,
AUTHOR = {Mahmood Hussain Mir, Sanjay Jamwal, Ummer Iqbal, Abolfazl Mehbodniya, Julian Webber, Umar Hafiz Khan},
TITLE = {A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2529--2565},
URL = {http://www.techscience.com/CMES/v137n3/53722},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.027173}
}