Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

• Published in: IEEE access Vol. 9; pp. 17787 - 17802
• Main Authors: Ye, Hua, Wu, Peiliang, Zhu, Tianru, Xiao, Zhongxiang, Zhang, Xie, Zheng, Long, Zheng, Rongwei, Sun, Yangjie, Zhou, Weilong, Fu, Qinlei, Ye, Xinxin, Chen, Ali, Zheng, Shuang, Heidari, Ali Asghar, Wang, Mingjing, Zhu, Jiandong, Chen, Huiling, Li, Jifa
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational and Artificial Intelligence; Computers and Information Processing; coronavirus; Coronaviruses; COVID-19; Decision making; Diagnostic systems; disease diagnosis; Diseases; feature selection; fuzzy K-nearest neighbor; Harris hawk optimization; Machine learning; Medical diagnostic imaging; Optimization; Performance indices; Prediction models; Predictive models; Signs and symptoms; Support vector machines; Viral diseases; COVID-19; coronavirus; disease diagnosis; fuzzy K-nearest neighbor; Harris hawk optimization; feature selection
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3052835

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.