Employing data mining techniques to classify Covid-19 pandemic

• Published in: AIP conference proceedings Vol. 3036; no. 1
• Main Authors: Shanshool, Abeer M., Bouchakwa, Mariam, Amor, Ikram Amous-Ben
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 15.03.2024
• Subjects: Accuracy; Algorithms; Artificial neural networks; Comparative studies; Data mining; New technology; Support vector machines
• ISSN: 0094-243X; 1935-0465; 1551-7616; 1551-7616
• DOI: 10.1063/5.0196328

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms.