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A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients

Author

Listed:
  • Prabh Deep Singh

    (Punjabi University)

  • Rajbir Kaur

    (Punjabi University)

  • Kiran Deep Singh

    (IKG Punjab Technical University)

  • Gaurav Dhiman

    (Punjabi University)

Abstract

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier’s accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.

Suggested Citation

  • Prabh Deep Singh & Rajbir Kaur & Kiran Deep Singh & Gaurav Dhiman, 2021. "A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients," Information Systems Frontiers, Springer, vol. 23(6), pages 1385-1401, December.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:6:d:10.1007_s10796-021-10132-w
    DOI: 10.1007/s10796-021-10132-w
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    References listed on IDEAS

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    1. Patricia Baudier & Galina Kondrateva & Chantal Ammi & Victor Chang & Francesco Schiavone, 2021. "Patients’ perceptions of teleconsultation during COVID-19: a cross-national study," Post-Print hal-03052149, HAL.
    2. Baudier, Patricia & Kondrateva, Galina & Ammi, Chantal & Chang, Victor & Schiavone, Francesco, 2021. "Patients’ perceptions of teleconsultation during COVID-19: A cross-national study," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. Abdel-Basset, Mohamed & Chang, Victor & Nabeeh, Nada A., 2021. "An intelligent framework using disruptive technologies for COVID-19 analysis," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
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    Cited by:

    1. Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.
    2. Abderrazek Azri & Cécile Favre & Nouria Harbi & Jérôme Darmont & Camille Noûs, 2023. "Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning," Information Systems Frontiers, Springer, vol. 25(5), pages 1795-1810, October.
    3. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.

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