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.

翻译：COVID-19大流行疾病对全球几乎所有国家产生了巨大影响，导致众多医院因新冠病例激增而不堪重负。由于医疗资源有限，如何合理分配这些资源成为极其关键的问题。此外，不确定性是影响决策的重要因素，尤其在医疗领域。为应对这一挑战，我们采用模糊逻辑（FL）作为处理高不确定性与复杂系统建模的最适方法之一，旨在利用FL的优势对需要ICU治疗的患者进行决策。本研究提出一种区间二型模糊专家系统，用于预测COVID-19患者的ICU入院需求。针对该预测任务，我们还开发了自适应神经模糊推理系统（ANFIS）。最后，将这些模糊系统的结果与朴素贝叶斯（NB）、案例推理（CBR）、决策树（DT）及K近邻（KNN）等经典分类方法进行对比。结果表明，二型模糊专家系统与ANFIS模型在准确率和F-measure指标上均具有与其他系统建模技术相竞争的性能。