The Comparison of SVM and ANN Classifier for COVID-19 Prediction
Abstract
Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.
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References
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