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COVID-19 Features Detection Using Machine Learning Models and Classifiers

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The Science behind the COVID Pandemic and Healthcare Technology Solutions

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 15))

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Abstract

Different machine learning techniques and approaches were implemented to detect the features of COVID-19, from chest X-Ray and CT medical images, as well as to identify them from other similar human-being lungs infection diseases. In this work, Logistic Regression, Neural Networks, Random Forests, Decision Trees, kNN, and CN2 Rule Induction are the machine learning models and classifiers that were utilized to perform such detection and identification. The entire process according to the importance of good parameters selection, and such performance was presented and emphasized at different phases of models analysis and visualization. In our presented method, the achieved classification accuracies were up to 95.5%. Our work was implemented using Orange software, as a visual-based tool, and dedicated for physicians with no experience in machine learning algorithms and programming languages.

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Authors and Affiliations

  1. Electrical and Computer Eng. Dept., Portland State University, Portland, USA

    Ali Al-Bayaty & Marek Perkowski

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  1. Ali Al-Bayaty
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  2. Marek Perkowski
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Correspondence to Ali Al-Bayaty .

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Editors and Affiliations

  1. School of Information Technology, Deakin University, Burwood, VIC, Australia

    Sasan Adibi

  2. Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia

    Abbas Rajabifard

  3. Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC, Australia

    Sheikh Mohammed Shariful Islam

  4. Princess Alexandra Hospital, Queensland Health, Brisbane, QLD, Australia

    Alireza Ahmadvand

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Al-Bayaty, A., Perkowski, M. (2022). COVID-19 Features Detection Using Machine Learning Models and Classifiers. In: Adibi, S., Rajabifard, A., Shariful Islam, S.M., Ahmadvand, A. (eds) The Science behind the COVID Pandemic and Healthcare Technology Solutions. Springer Series on Bio- and Neurosystems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-031-10031-4_18

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  • DOI: https://doi.org/10.1007/978-3-031-10031-4_18

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  • Online ISBN: 978-3-031-10031-4

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