An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization

• URL: http://arxiv.org/abs/2301.05296v1
• Date: Sun, 25 Dec 2022 19:52:18 GMT
• Title: An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization
• Authors: Abubakr Issa, Yossra Ali, Tarik Rashid
• Abstract summary: This study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) for Covid-19 classification. The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM, 98% and 98% accuracy with two classifiers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO's performance using the Salp algorithm's power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN.

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