WOA-COVID-19: Whale Optimization Algorithm for Selection of Multi-Examination Features based on COVID-19 Infections
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| Title: | WOA-COVID-19: Whale Optimization Algorithm for Selection of Multi-Examination Features based on COVID-19 Infections |
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| Authors: | Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Zaid Abdi Alkareem Alyasseri, Dawood Zahi Khutar, Osama Ahmad Alomari |
| Source: | Mesopotamian Journal of Computer Science. 2025:172-185 |
| Publisher Information: | Mesopotamian Academic Press, 2025. |
| Publication Year: | 2025 |
| Description: | Since its emergence in late 2019, COVID-19 (Coronavirus Disease 2019) has become one of the most critical global health threats, claiming millions of lives and placing many more at serious risk. The complexity of diagnosing COVID-19 lies in the wide range of clinical and examination features involved, prompting researchers to explore various advanced diagnostic methods. However, one of the main challenges is identifying the most relevant features that can streamline and improve diagnostic accuracy. In this study, we propose a feature selection approach based on the Whale Optimization Algorithm (WOA) to identify key examination indicators associated with COVID-19. We used a dataset of 78 patients that included 25 features, covering demographics, symptoms, vital signs, laboratory findings, and chronic health conditions. The WOA was applied as a single-objective optimization technique to select the most informative features. These selected features were then used with the K-Nearest Neighbors (KNN) algorithm to classify patients into three categories of severity: mild, moderate, and severe. To evaluate the effectiveness of WOA, we compared it against six other well-established metaheuristic algorithms: Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Firefly Algorithm (FFA), and the BAT algorithm. Results showed that WOA successfully reduced the feature set from 25 to just 6 key features while achieving a high classification accuracy of 92.5%. It also demonstrated strong robustness, as reflected in its low standard deviation compared to other methods. Overall, the proposed WOA-COVID-19 framework proved to be a highly effective and efficient solution for feature selection in the context of COVID-19 diagnosis. |
| Document Type: | Article |
| ISSN: | 2958-6631 |
| DOI: | 10.58496/mjcsc/2025/010 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........d930e7b6c223b7e32f14b14a9073468a |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Since its emergence in late 2019, COVID-19 (Coronavirus Disease 2019) has become one of the most critical global health threats, claiming millions of lives and placing many more at serious risk. The complexity of diagnosing COVID-19 lies in the wide range of clinical and examination features involved, prompting researchers to explore various advanced diagnostic methods. However, one of the main challenges is identifying the most relevant features that can streamline and improve diagnostic accuracy. In this study, we propose a feature selection approach based on the Whale Optimization Algorithm (WOA) to identify key examination indicators associated with COVID-19. We used a dataset of 78 patients that included 25 features, covering demographics, symptoms, vital signs, laboratory findings, and chronic health conditions. The WOA was applied as a single-objective optimization technique to select the most informative features. These selected features were then used with the K-Nearest Neighbors (KNN) algorithm to classify patients into three categories of severity: mild, moderate, and severe. To evaluate the effectiveness of WOA, we compared it against six other well-established metaheuristic algorithms: Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Firefly Algorithm (FFA), and the BAT algorithm. Results showed that WOA successfully reduced the feature set from 25 to just 6 key features while achieving a high classification accuracy of 92.5%. It also demonstrated strong robustness, as reflected in its low standard deviation compared to other methods. Overall, the proposed WOA-COVID-19 framework proved to be a highly effective and efficient solution for feature selection in the context of COVID-19 diagnosis. |
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| ISSN: | 29586631 |
| DOI: | 10.58496/mjcsc/2025/010 |