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Journal of Medical Signals and Sensors، جلد ۱۲، شماره ۴، صفحات ۰-۰
عنوان فارسی
چکیده فارسی مقاله
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عنوان انگلیسی Classification of COVID-19 Individuals Using Adaptive Neuro-Fuzzy Inference System
چکیده انگلیسی مقاله The COVID-19 has become an important health issue in the world and has endangered human health. The purpose of this research is to use an intelligent system model of adaptive neuro-fuzzy inference system (ANFIS) using twelve variables of input for the diagnosis of COVID-19. The evaluation of the model was performed using the information of 500 patients referred to and suspected of the COVID-19. Three hundred and fifty people were used as training data and 150 people were used as test and validation data. Information on 12 important parameters of COVID-19 such as fever, cough, headache, respiratory rate, Ct-chest, medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise was also reported in patients with severe disease. ANFIS identified COVID-19 in accuracy, sensitivity, and specificity with more than 95%, 94%, and 95%, respectively, which indicates the high efficiency of the system in the correct diagnosis of individuals. The proposed system accurately detected more than 95% COVID-19 as well as mild, moderate, and acute severity. Due to the time-constraint, limitations, and error of COVID-19 diagnostic tools, the proposed system can be used in high-precision primary detection, as well as saving time and cost.
کلیدواژه‌های انگلیسی مقاله Accuracy, adaptive, COVID-19, diagnosis, neuro-fuzzy
نویسندگان مقاله | Mohammad Dehghandar
Departments of Applied Mathematics, Payame Noor University, Tehran, Iran


| Samaneh Rezvani


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/640
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زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Short Reports
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