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Enhancing COVID-19 classification accuracy with a Hybrid SVM-LR Model

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General Information
Title
Enhancing COVID-19 classification accuracy with a Hybrid SVM-LR Model
Type
Article
Main Author
Nurul Hila binti Zainuddin
Subject
Q Science (General)
ISSN
2306-5354
Language Code
eng
Bibliographic Details
Place of Production
Tanjung Malim
Publisher
Fakulti Sains dan Matematik
Year of publication
2023
Notes
Bioengineering
Degree Type
Not related
Corporate Name
Universiti Pendidikan Sultan Idris

Abstract

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.

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