Classification of COVID-19 Individuals Using Adaptive Neuro-Fuzzy Inference System

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.