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

被引：0

作者：

Sadat Asl, A.A. ^{[1
]}

Ershadi, M.M. ^{[1
,2
]}

Sotudian, S. ^{[1
,2
]}

Li, X. ^{[1
]}

Dick, S. ^{[1
]}

机构：

[1] Department of Electrical and Computer Engineering, University of Alberta, Edmonton,AB, Canada

[2] Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

来源：

Intelligent Decision Technologies | 2022年 / 16卷 / 01期

关键词：

Nearest neighbor search - Uncertainty analysis - Decision trees - Fuzzy inference - Forecasting - Diagnosis - Fuzzy neural networks - Fuzzy systems - Case based reasoning - Computer circuits;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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 these issues, 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 the 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 diagnosis systems. © 2022 - IOS Press. All rights reserved.

引用

页码：159 / 168