# Fuzzy Expert Systems for Prediction of ICU Admission in Patients with COVID-19

arXiv: https://arxiv.org/abs/2104.12868 · HTML: https://mlsanity.com/paper/2104.12868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions](https://mlsanity.com/paper/2006.10532/) (2020)
- [Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control](https://mlsanity.com/paper/2104.03893/) (2021)
- [Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System](https://mlsanity.com/paper/1910.13574/) (2019)
- [A New Validity Index for Fuzzy-Possibilistic C-Means Clustering](https://mlsanity.com/paper/2005.09162/) (2020)
- [Fuzzy expert system for prediction of prostate cancer](https://mlsanity.com/paper/1812.00236/) (2018)
- [Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Clustering for Gene Expression Data Analysis](https://mlsanity.com/paper/2101.00304/) (2021)

## Abstract (arXiv)

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.