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DOI: 10.14569/IJACSA.2020.0110518
PDF

Machine Learning and Statistical Modelling for Prediction of Novel COVID-19 Patients Case Study: Jordan

Author 1: Ebaa Fayyoumi
Author 2: Sahar Idwan
Author 3: Heba AboShindi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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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%.

Keywords: Novel COVID-19; machine learning; logistic regression; support vector machine; multi-layer perceptron

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}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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