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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

Saud S. Alotaibi1, Amal Al-Rasheed2, Sami Althahabi3, Manar Ahmed Hamza4,*, Abdullah Mohamed5, Abu Sarwar Zamani4, Abdelwahed Motwakel4, Mohamed I. Eldesouki6

1 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University,Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
5 Research Centre, Future University in Egypt, New Cairo, 11745, Egypt
6 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2022, 73(2), 3305-3318. https://doi.org/10.32604/cmc.2022.029385

Abstract

Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology dataset. In the proposed MOKELM-CPED model, the data first undergoes pre-processing to transform the medical data into useful format. Followed by, data classification process is performed by following Kernel Extreme Learning Machine (KELM) model. Finally, Symbiotic Organism Search (SOS) optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection efficiency. In order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner, a comprehensive experimental analysis was conducted and the results were inspected under diverse aspects. The outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

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Cite This Article

APA Style
Alotaibi, S.S., Al-Rasheed, A., Althahabi, S., Hamza, M.A., Mohamed, A. et al. (2022). Optimal kernel extreme learning machine for COVID-19 classification on epidemiology dataset. Computers, Materials & Continua, 73(2), 3305-3318. https://doi.org/10.32604/cmc.2022.029385
Vancouver Style
Alotaibi SS, Al-Rasheed A, Althahabi S, Hamza MA, Mohamed A, Zamani AS, et al. Optimal kernel extreme learning machine for COVID-19 classification on epidemiology dataset. Comput Mater Contin. 2022;73(2):3305-3318 https://doi.org/10.32604/cmc.2022.029385
IEEE Style
S.S. Alotaibi et al., "Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset," Comput. Mater. Contin., vol. 73, no. 2, pp. 3305-3318. 2022. https://doi.org/10.32604/cmc.2022.029385



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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