Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with cases. As medical resources are limited, deciding proper allocation of these is very crucial issue. Besides, uncertainty major factor can affect decisions, especially in fields. To cope issues, we use fuzzy logic (FL) as one most suitable methods modeling systems high and complexity. We intend to make advantages FL decisions cases need treat ICU. In this study, an interval type-2 expert system proposed for prediction ICU admission patients. For task, also developed adaptive neuro-fuzzy inference (ANFIS). Finally, results compared some well-known classification such Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), K Nearest Neighbor (KNN). show ANFIS models perform competitively terms accuracy F-measure other diagnosis systems.