A Comparative Study of Logistic Regression and Support Vector Machine for COVID-19 Symptom Prediction - Ferdiansyah, Ferdiansyah; Putra, Briandy Tri; Yulianingsih, Evi; Fatmasari, Fatmasari; Idham, Muhammad (International Journal of Artificial Intelligence and Science, 2024)
The rapid spread of COVID-19 has created a critical need for accurate and efficient tools to predict symptoms and aid in early diagnosis. This study aims to compare the effectiveness of two machine learning models, Logistic Regression and Support Vector Machine (SVM), in predicting COVID-19 symptoms based on patient data. The dataset used contains key COVID-19 symptoms, which were processed and modeled using both techniques. Logistic Regression was evaluated alongside SVM using three different kernels: Linear, Sigmoid, and Radial Basis Function (RBF). The models' performance was measured using the Confusion Matrix to assess accuracy. Logistic Regression achieved an accuracy of 96.78%, while the SVM with the RBF Kernel slightly outperformed it with an accuracy of 96.85%. The SVM with the Sigmoid Kernel performed the least effectively, with an accuracy of 95.19%. These findings suggest that both models are highly effective for symptom prediction, with the RBF Kernel showing the best overall performance in handling complex, non-linear data patterns.