An Efficient Hybrid Classification Approach for COVID-19 Based on Harris Hawks Optimization and Salp Swarm Optimization

Authors

DOI:

https://doi.org/10.3991/ijoe.v18i13.33195

Keywords:

Feature selection, Hybrid Swarm intelligence, classification, Covid-19, medical image

Abstract


Feature selection can be defined as one of the pre-processing steps that decreases 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 Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA). The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the grey wolf optimizer (GWO), utilizing 280 2D X-ray images from the Posteroanterior (PA) chest view dataset for normal and covid-19 patients. A total of three performance metrics (Recall, Precision, F1) were employed in the studies with Support vector machines (SVMs), k-Nearest Neighbor (KNN), and XGBoost as classifiers. The suggested algorithm outperforms SVM by 96%, and two classifiers, XGboost and KNN, by 98%.

Author Biographies

Yossra Ali, University of Technology , Baghdad , Iraq

Assistant Professor Dr. Yossra Hussain Ali .She received her B.Sc , M.Sc and Ph.D. degrees in 1996, 2002 and 2006 respectively in Computer Science from Iraq, University of technology, department of Computer Sciences. She Joined the University of Technology, Iraq in 1997. During her postgraduate studies she worked on Computer Network, Information systems, Agent Programming and Image Processing, She has some experience in Artificial Intelligent and Computer Data Security, She Reviewer at many conference and journals, she supervision of undergraduate and postgraduate ( Ph.D. and M.Sc.) dissertations for many students in Computer sciences, she has a number of professional certificates, Yossra has published in well regarded journals.

Tarik Rashid , University of kurdidtan Hewler, KRG, Iraq

Tarik Ahmed Rashid: received his Ph.D. in Computer Science and Informatics degree from College of Engineering, Mathematical and Physical Sciences, University College Dublin (UCD) in 2001-2006. He pursued his Post-Doctoral Fellow at the Computer Science and Informatics School, College of Engineering, Mathematical and Physical Sciences, University College Dublin (UCD) from 2006-2007. He joined the University of Kurdistan Hewlêr (UKH) in 2017. He has also been included in the prestigious Stanford University list with 2.7% of the best world researchers for the year 2020.

Covr iJOE Vol. 18 No. 13 (2022)

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Published

2022-10-19

How to Cite

Issa, A., Ali, Y., & Rashid , T. (2022). An Efficient Hybrid Classification Approach for COVID-19 Based on Harris Hawks Optimization and Salp Swarm Optimization. International Journal of Online and Biomedical Engineering (iJOE), 18(13), pp. 113–130. https://doi.org/10.3991/ijoe.v18i13.33195

Issue

Vol. 18 No. 13 (2022)

Section

Papers

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Copyright (c) 2022 Abubakr Issa, Dr.Yossra H. Ali

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