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