Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System
نویسندگان
چکیده
The undergoing research aims to address the problem of COVID-19 which has turned out be a global pandemic. Despite developing some successful vaccines, pace not overcome so far. Several studies have been proposed in literature this regard, present study is unique terms its dynamic nature adapt rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related travelling, diseases/medications interactions with confirmed patients, rule-based system (FRBS) identifies presence/absence disease. This can greatly help healthcare professionals as well laymen disease identification. main motivation paper reduce pressure health services due frequent test assessment requests, patients do anytime without need make reservations. findings are that there relationship between indicate probability presence such high difficulty breathing, cough, sore throat, many more. By knowing common symptoms, we developed functions for these model generated distinguish infected non-infected people survey data collected. gave an accuracy 88.78%, precision 72.22%, sensitivity 68.42%, specificity 93.67%, f1-score 69.28%.
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ژورنال
عنوان ژورنال: Mathematical modelling of engineering problems
سال: 2021
ISSN: ['2369-0739', '2369-0747']
DOI: https://doi.org/10.18280/mmep.080517