Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8362
Title: Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization
Authors: Shi, Beibei 
Ye, Hua 
Heidari, Ali Asghar 
Zheng, Long 
Hu, Zhongyi 
Chen, Huiling 
Turabieh, Hamza 
Mafarja, Majdi 
Wu, Peiliang 
Keywords: Coronavirus Disease 2019;Coagulation index;Data mining;Brain Storm optimization algorithm;Machine learning;Support vector machine;Harris Hawks optimization;Computational intelligence;Evolutionary computation
Issue Date: 2022
Publisher: Journal of King Saud University - Computer and Information Sciences
Abstract: Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.
URI: http://hdl.handle.net/20.500.11889/8362
DOI: 10.1016/j.jksuci.2021.09.019
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