1. Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods
- Author
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Xinxin Ye, Ali Asghar Heidari, Zhongxiang Xiao, Huiling Chen, Jiandong Zhu, Ali Chen, Shuang Zheng, Rongwei Zheng, Jifa Li, Tianru Zhu, Long Zheng, Hua Ye, Peiliang Wu, Qinlei Fu, Xie Zhang, Weilong Zhou, Yangjie Sun, and Mingjing Wang
- Subjects
General Computer Science ,Coronavirus disease 2019 (COVID-19) ,Computer science ,coronavirus ,disease diagnosis ,Stability (learning theory) ,Computers and Information Processing ,Feature selection ,02 engineering and technology ,Machine learning ,computer.software_genre ,Fuzzy logic ,feature selection ,Immune system ,Prediction methods ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,General Materials Science ,business.industry ,General Engineering ,Fuzzy k nearest neighbor ,COVID-19 ,020206 networking & telecommunications ,fuzzy K-nearest neighbor ,Harris hawk optimization ,TK1-9971 ,Support vector machine ,Computational and Artificial Intelligence ,020201 artificial intelligence & image processing ,Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,computer - Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
- Published
- 2021
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