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

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.

Automated summary

This study proposes a novel hybrid model based on SVM and LR for predicting small events per variable. The hybrid model outperforms SVM and LR in terms of accuracy, mean squared error, and root mean squared error. This hybrid model is particularly important for medical institutions and practitioners in the face of future pandemics.