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Adaptive Neuro Fuzzy Inference System for Likelihood of Admission to ICU for COVID-19 Patients
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Copyright: 2023
Pages: 20
Source title:
Controlling Epidemics With Mathematical and Machine Learning Models
Source Author(s)/Editor(s): Abraham Varghese (University of Technology and Applied Sciences, Muscat, Oman), Eduardo M. Lacap, Jr. (University of Technology and Applied Sciences, Muscat, Oman), Ibrahim Sajath (University of Technology and Applied Sciences, Muscat, Oman), M. Kamal Kumar (University of Technology and Applied Sciences, Muscat, Oman)and Shajidmon Kolamban (University of Technology and Applied Sciences, Muscat, Oman)
DOI: 10.4018/978-1-7998-8343-2.ch010
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Abstract
The analysis of epidemiological data is critical to disease prevention and control programs geared toward improving, promoting, and protecting the health of communities. Various decision-making support systems have been modelled using artificial neural networks and fuzzy inferences. A neuro-fuzzy inference system based on the Takagi-Sugeno system was developed in the early 1990s that integrates the advantages of neural networks with fuzzy logic principles, such as self-learning and knowledge representation. Adaptive neuro-fuzzy inference systems are devised and evaluated here as means of characterizing the severity of a laboratory-confirmed COVID-19 case. The authors describe the underlying architecture for ANFIS with various clustering approaches, including grid partitioning, subtractive clustering, and fuzzy c-means. A total of 385 cases with eight potential predictors is used to develop, validate, and evaluate the model.
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