Evaluating Three Machine Learning Classification Methods for Effective COVID-19 Diagnosis
Akbal Omran Salman, Oana Geman
Abstract
SARS-CoV2, which produces COVID-19, has spread worldwide. Since the number of patients is rising daily, it requires time to evaluate laboratory data, limiting treatment and discoveries. Such restrictions necessitate a clinical decision-making tool with predictive algorithms. Predictive algorithms help healthcare systems by spotting disorders. This study uses machine learning and laboratory data to predict COVID-19 patients. Recall, Precision, accuracy, and AUC ratings assessed our models' prediction performance. Models were verified with 10-fold cross-validation and train-test split methods using 18 laboratory data from 600 patients. This research compared three different classification approaches—Support Vector Machines (SVM), artificial neural networks (ANN), and k-Nearest Neighbors (k-NN). According to the findings, SVM achieved the most significant average accuracy (89.3%), followed by ANN (88.5%) and kNN (86.6%). The accuracy rates of all three approaches were relatively reasonable, with SVM being the best of the bunch. The results of this research indicate that classification using machine learning methods has the potential to be used in developing reliable COVID-19 diagnosis systems, thereby facilitating the fast and accurate diagnosis of COVID-19 cases and facilitating proper therapy and management of COVID-19 patients. More work might be done to refine these techniques and include them in useable diagnostic frameworks.