Modeling of self-assessment system of COVID-19 disease diagnosis using Type-2 Sugeno fuzzy inference system

Due to the continuation of the pandemic of Coronavirus in the whole world, the number of deaths has reached over one million, based on the World Health Organization reports. Early diagnosis of the illness can be a great assistance in order to break the chain of disease transmission. Nowadays, COVID-19 test kits are so limited in numbers, and expensive in terms of a cost, which slows down the diagnosis procedure and makes it difficult, thus, it is necessary to diagnose the disease in the early stages, to prevent its incidence. Therefore, we decided to propose a self-assessment method for COVID-19 disease, using a type-2 Sugeno fuzzy inference system, which causes conservation in time and costs. The system is prepared based on 98 rules, according to the World Health Organization instructions, using MATLAB software to simulate and diagnose the disease. The results show that Sugeno fuzzy with better correlation coefficient R 2 = 0.94 and error squared RMSE = 0.045, respectively, has acceptable accuracy for estimating and identifying COVID-19 disease. The self-assessment consequences are very promising and can prevent the further spread of the disease.