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Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease

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Abstract

Sustainable development is crucial for a prosperous future, but epidemic diseases like Coronavirus Disease 2019 (COVID-19) pose real and complex challenges. The global pandemic, declared by the WHO on March 11, 2020, has led to significant loss of life and challenges in public health, economy, food systems, and social life. This research article aims to develop a Machine Learning (ML) model to predict a patient’s COVID-19 diagnosis, as early diagnosis is fundamental for controlling the spread of COVID-19. An investigation of the literature and a research study is conducted to find suitable mathematical methods and measure how they impact prediction models. This paper analyses COVID-19 pandemic deaths, confirmed cases, and recovered individuals using Time Series Analysis (TSA) to study the disease's impacts and understand the TAS. A forecasting model can predict future COVID cases by analyzing trends in time-series and connecting global changes with government restrictions. Since higher predictive accuracy is the limitation of ensemble learning algorithms, better ML approaches are proposed here. Autocorrelation plots clearly showed the results executed for the considered objectives. The hybrid ARIMA algorithm proposed in this work proved adequate results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Computing, Institute of Science and Technology (Deemed to Be University), Vel Tech Rangarajan Dr. Sagunthala R&D, Chennai, Tamil Nadu, 600062, India

    Senthil Kumar Nramban Kannan

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India

    Bhanu Prakash Kolla

  3. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

    Sudhakar Sengan

  4. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India

    Rajendiran Muthusamy

  5. Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Trichy, Tamil Nadu, 621112, India

    Raja Manikandan

  6. Charotar University of Science and Technology, Gujarat, 388421, India

    Kanubhai K. Patel

  7. Department of Computer Science and Engineering, Management and Gramothan (SKIT), Swami Keshvanand Institute of Technology, Jaipur, Rajasthan, 302017, India

    Pankaj Dadheech

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  1. Senthil Kumar Nramban Kannan
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  2. Bhanu Prakash Kolla
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  3. Sudhakar Sengan
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  4. Rajendiran Muthusamy
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  5. Raja Manikandan
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  6. Kanubhai K. Patel
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  7. Pankaj Dadheech
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Correspondence to Bhanu Prakash Kolla.

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This article is part of the topical collection “Soft Computing in Engineering Applications” guest edited by Kanubhai K. Patel.

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Nramban Kannan, S.K., Kolla, B.P., Sengan, S. et al. Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease. SN COMPUT. SCI. 5, 181 (2024). https://doi.org/10.1007/s42979-023-02464-y

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