Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

• Published in: Bioengineering
• Main Author: Nordin N.I.; Mustafa W.A.; Lola M.S.; Madi E.N.; Kamil A.A.; Nasution M.D.; K. Abdul Hamid A.A.; Zainuddin N.H.; Aruchunan E.; Abdullah M.T.
• Format: Article
• Language: English
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178102316&doi=10.3390%2fbioengineering10111318&partnerID=40&md5=7ba5b357a436e788c4c62a24798f8567

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. © 2023 by the authors.