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An augmented Snake Optimizer for diseases and COVID-19 diagnosis.
- Source :
- Biomedical Signal Processing & Control; Jul2023, Vol. 84, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
Abstract
- Feature Selection (FS) techniques extract the most recognizable features for improving the performance of classification methods for medical applications. In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the Snake Optimizer (SO) are introduced. The binary SO, called BSO, is built based on an S-shape transform function to handle the binary discrete values in the FS domain. To improve the exploration of the search space by BSO, three evolutionary crossover operators (i.e., one-point crossover, two-point crossover, and uniform crossover) are incorporated and controlled by a switch probability. The two newly developed FS algorithms, BSO and BSO-CV, are implemented and assessed on a real-world COVID-19 dataset and 23 disease benchmark datasets. According to the experimental results, the improved BSO-CV significantly outperformed the standard BSO in terms of accuracy and running time in 17 datasets. Furthermore, it shrinks the COVID-19 dataset's dimension by 89% as opposed to the BSO's 79%. Moreover, the adopted operator on BSO-CV improved the balance between exploitation and exploration capabilities in the standard BSO, particularly in searching and converging toward optimal solutions. The BSO-CV was compared against the most recent wrapper-based FS methods; namely, the hyperlearning binary dragonfly algorithm (HLBDA), the binary moth flame optimization with Lévy flight (LBMFO-V3), the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC), as well as four filter methods with an accuracy of more than 90% in most benchmark datasets. These optimistic results reveal the great potential of BSO-CV in reliably searching the feature space. • Binary Snake Optimizer (BSO) is proposed for feature selection problems. • Greedy Crossover operators are integrated with BSO, referred to as BSO-CV. • BSO and BSO-CV are tested on 23 medical datasets and a COVID-19 real dataset. • Experimental results demonstrate the merits of the proposed BSO-CV. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 17468094
- Volume :
- 84
- Database :
- Supplemental Index
- Journal :
- Biomedical Signal Processing & Control
- Publication Type :
- Academic Journal
- Accession number :
- 163974265
- Full Text :
- https://doi.org/10.1016/j.bspc.2023.104718