@Article{cmc.2020.011892,
AUTHOR = {Tahir Abbas Khan, Sagheer Abbas, Allah Ditta, Muhammad Adnan Khan, Hani Alquhayz, Areej Fatima, Muhammad Farhan Khan},
TITLE = {IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference  System for Diagnosis of COVID-19},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2591--2605},
URL = {http://www.techscience.com/cmc/v65n3/40189},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011892}
}