Abstract: As of December 2019, the world’s view on life has been changed due to ongoing COVID-19 pandemic. This requires the use of all kinds of technology to help identify coronavirus patients and control the spread of this disease. In this paper, an online questionnaire was developed as a tool to collect data. This data was used as an input for various prediction models based on statistical model (Logistic Regression, LR) and machine learning model (Support Vector Machine, SVM, and Multi-Layer Perceptron, MLP). These models were utilized to predict potential patients of COVID-19 based on their signs and symptoms. The MLP has shown the best accuracy (91.62%) compared to the other models. Meanwhile, the SVM has shown the best precision 91.67%.

Ebaa Fayyoumi, Sahar Idwan and Heba AboShindi, “Machine Learning and Statistical Modelling for Prediction of Novel COVID-19 Patients Case Study: Jordan” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110518

@article{Fayyoumi2020,
title = {Machine Learning and Statistical Modelling for Prediction of Novel COVID-19 Patients Case Study: Jordan},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110518},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110518},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Ebaa Fayyoumi and Sahar Idwan and Heba AboShindi}
}