IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic

Nouf Abdullah Almujally; Turki Aljrees; Muhammad Umer; Oumaima Saidani; Danial Hanif; Nihal Abuzinadah; Khaled Alnowaiser; Imran Ashraf

doi:10.7717/peerj-cs.1493

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic

PeerJ Comput Sci. 2023 Oct 19:9:e1493. doi: 10.7717/peerj-cs.1493. eCollection 2023.

Authors

Nouf Abdullah Almujally¹, Turki Aljrees², Muhammad Umer³, Oumaima Saidani¹, Danial Hanif³, Nihal Abuzinadah⁴, Khaled Alnowaiser⁵, Imran Ashraf⁶

Affiliations

¹ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
² Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
³ Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
⁴ Faculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Sauid Arabia.
⁵ Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁶ Information and Communication Engineering, Yeungnam University, Gyeongsan si, Daegu, South Korea.

Abstract

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19. In addition, a real-time solution is provided to identify and monitor COVID-19 patients. The proposed framework comprises data collection using IoT-based devices, a health or quarantine center, a data warehouse for artificial intelligence (AI)-based analysis, and healthcare professionals to provide treatment. The efficacy of several machine learning models is also analyzed for the prediction of the severity level of COVID-19 patients using real-time IoT data and a dataset named 'COVID Symptoms Checker'. The proposed ensemble model combines random forest and extra tree classifiers using a soft voting criterion and achieves superior results with a 0.922 accuracy score. The use of IoT applications is found to support medical professionals in investigating the features of the contagious disease and support managing the COVID pandemic more efficiently.

Keywords: Deep learning; Internet of Medical Things (MIoT); IoT application; Sensors; Smart healthcare system.

Grants and funding

The funding of this work was provided by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The funders participated in data curation, formal analysis and investigation part of the paper. The funders had no role in study design, decision to publish, or preparation of the manuscript.