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An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images

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Applied Informatics (ICAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1455))

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Abstract

The pandemic produced by coronavirus2 (COVID-19) has confined the world, and avoiding close human contact is still suggested to combat the outbreak although the vaccination campaigns. It is expectable that emerging technologies have prominent roles to play during this pandemic, and the use of Artificial Intelligence (AI) has been proved useful in this direction. The use of AI by researchers in developing novel models for diagnosis, classification, and prediction of COVID-19 has really assist reduce the spread of the outbreak. Therefore, this paper proposes a machine learning diagnostic system to combat the spread of COVID-19. Four machine learning algorithms: Random Forest (RF), XGBoost, and Light Gradient Boosting Machine (LGBM) were used for quick and better identification of potential COVID-19 cases. The dataset used contains COVID-19 symptoms and selects the relevant symptoms of the diagnosis of a suspicious individual. The experiments yielded the LGBM leading with an accuracy of 0.97, recall of 0.96, precision of 0.97, F1-Score of 0.96, and ROC of 0.97 respectively. The real-time data capture would effectively diagnose and monitor COVID-19 patients, as revealed by the results.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

  2. Department of Computer Engineering, Ladoke Akintola University of Technology LAUTECH, Ogbomoso, Nigeria

    Sunday Adeola Ajagbe

  3. Salford University, The Greater Manchester, Manchester, UK

    Matthew A. Oladipupo

  4. Mobile and e-Computing Research Group, Faculty of Computing and Informatics, LAUTECH, Oyo State, Ogbomoso, Nigeria

    Jimmisayo A. Awokola

  5. Department of Computer Science, University of Abuja, Abuja, Nigeria

    Olakunle S. Afolabi

  6. The Federal Polytechnic, Ilaro, Nigeria

    Timothy O. Mathew

  7. Department of Computer Studies, The Polytechnic Ibadan, Ibadan, Nigeria

    Yetunde J. Oguns

Authors
  1. Joseph Bamidele Awotunde
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  2. Sunday Adeola Ajagbe
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  3. Matthew A. Oladipupo
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  4. Jimmisayo A. Awokola
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  5. Olakunle S. Afolabi
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  6. Timothy O. Mathew
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  7. Yetunde J. Oguns
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Corresponding author

Correspondence to Sunday Adeola Ajagbe .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogotá, Colombia

    Hector Florez

  2. Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Buenos Aires, Argentina

    Ma Florencia Pollo-Cattaneo

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Awotunde, J.B. et al. (2021). An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_23

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  • DOI: https://doi.org/10.1007/978-3-030-89654-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89653-9

  • Online ISBN: 978-3-030-89654-6

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