Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model.
Noor Ilanie Nordin, Wan Azani Mustafa, Muhamad Safiih Lola, Elissa Nadia Madi, Anton Abdulbasah Kamil, Marah Doly Nasution, Abdul Aziz K Abdul Hamid, Nurul Hila Zainuddin, Elayaraja Aruchunan, Mohd Tajuddin Abdullah
Author Information
Noor Ilanie Nordin: Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia.
Wan Azani Mustafa: Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia. ORCID
Muhamad Safiih Lola: Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia. ORCID
Elissa Nadia Madi: Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Besut Campus, Besut 22200, Terengganu, Malaysia. ORCID
Anton Abdulbasah Kamil: Faculty of Economics, Administrative and Social Sciences, Istanbul Gelisim University, Cihangir Mah. Şehit Jandarma Komando Er Hakan Öner Sk. No:1 Avcılar, İstanbul 34310, Turkey. ORCID
Marah Doly Nasution: Faculty of Teacher and Education, University Muhammadiyah Sumatera Utara, Jl. Kapten Muchtar Basri No.3, Glugur Darat II, Kec. Medan Tim., Kota Medan 20238, Sumatera Utara, Indonesia.
Abdul Aziz K Abdul Hamid: Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia.
Nurul Hila Zainuddin: Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 53900, Perak Darul Ridzuan, Malaysia.
Elayaraja Aruchunan: Department of Decision Science, Faculty of Business and Economics, University Malaya, Kuala Lumpur 50603, Malaysia. ORCID
Mohd Tajuddin Abdullah: Fellow Academy of Sciences Malaysia, Level 20, West Wing Tingkat 20, Menara MATRADE, Jalan Sultan Haji Ahmad Shah, Kuala Lumpur 50480, Malaysia.
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.
