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In IEEE access : practical innovations, open solutions

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.

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


COVID-19, Harris hawk optimization, coronavirus, disease diagnosis, feature selection, fuzzy K-nearest neighbor