Review : Machine and deep learning methods in Malaysia for COVID-19

• Main Authors: Mohammed Adam Kunna, Azrag, Jasni, Mohamad Zain, Tuty Asmawaty, Abdul Kadir, Marina, Yusoff, Hai, Tao
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/38363/1/Review_Machine%20and%20deep%20learning%20methods%20in%20Malaysia%20for%20COVID-19.pdf; http://umpir.ump.edu.my/id/eprint/38363/; https://doi.org/10.11591/ijeecs.v31.i1.pp514-520; https://doi.org/10.11591/ijeecs.v31.i1.pp514-520

The global pandemic of the coronavirus disease COVID-19 has impacted a variety of operations. This dilemma is also attributable to the lockdown measures taken by the afflicted nations. The entire or partial shutdown enacted by nations across the globe affected the majority of hospitals and clinics until the pandemic was contained. The judgements made by the authorities of each impacted nation vary based on a number of variables, including the nation's severity of reported cases, the availability of vaccines, beds in intensive care unit (ICU), staff number, patient number, and medicines. Consequently, this work offers a thorough analysis of the most recent machine learning (ML) and deep learning (DL) approaches for COVID-19 that can assist the medical field in offering quick and exact COVID-19 diagnosis in Malaysia. This research aims to review the machine learning and deep learning methods that were used to help diagnose COVID-19 in Malaysia.