Vol.65, No.3, 2020, pp.2591-2605, doi:10.32604/cmc.2020.011892
OPEN ACCESS
ARTICLE
IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19
  • Tahir Abbas Khan1, Sagheer Abbas1, Allah Ditta2, Muhammad Adnan Khan3, *, Hani Alquhayz4, Areej Fatima3, Muhammad Farhan Khan5
1 Department of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.
2 Department of Information Sciences, Division of Science & Technology, University of Education, Lahore, 54000, Pakistan.
3 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan.
4 Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, AlMajmaah, 11952, Saudi Arabia.
5 Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan.
* Corresponding Author: Muhammad Adnan Khan. Email: madnankhan@lgu.edu.pk.
(This article belongs to this Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)
Received 03 June 2020; Accepted 23 August 2020; Issue published 16 September 2020
Abstract
The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.
Keywords
IoMT, MERS-COV, Ct-chest, ESR/CRP, ABD (lgG), Fuzzy logic, HMFIS, WHO.
Cite This Article
Khan, T. A., Abbas, S., Ditta, A., Khan, M. A., Alquhayz, H. et al. (2020). IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19. CMC-Computers, Materials & Continua, 65(3), 2591–2605.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.