A type-2 fuzzy rule-based model for diagnosis of COVID-19

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods the diagnosis of COVID-19 has been developed. The developed consists three units. first unit produces positivity as percentage according respiratory rate, loss smell, body temperature values, second C-reactive protein, lymphocyte, D-dimer values obtained result tests. third unit, risks analysis results, which are outputs units, evaluated together is obtained. As evaluation trials with 60 different scenarios by physicians, it revealed detect risk 86.6% accuracy.