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Feature selection can be defined as one of the pre-processing steps thatdecrease the dimensionality of a dataset by identifying the most significantattributes while also boosting the accuracy of classification. For solvingfeature selection problems, this study presents a hybrid binary version of theHarris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA)(HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategyfor improving the basic HHO's performance using the Salp algorithm's power toselect the best fitness values. The HHOSSA was tested against two well-knownoptimization algorithms, the Whale Optimization Algorithm (WOA) and the Greywolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total offour performance metrics (Accuracy, Recall, Precision, F1) were employed in thestudies using three classifiers (Support vector machines (SVMs), k-NearestNeighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposedalgorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98%accuracy with two classifiers, XGboost and KNN.

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