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Comparative Analysis of Machine Learning Algorithms with Ensemble Techniques and Forecasting COVID-19 Cases in India

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Artificial Intelligence on Medical Data

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 37))

Abstract

Unanticipated information in December 2019 changed the world around us. A relatively contagious disease unfolded through the SARS-CoV-2 virus that travelled throughout the globe and was declared an epidemic by WHO in March 2020. The need of examining the scenario became the inducement behind this research. The assessment of COVID-19 in India is performed from 1 April 2020 to 20 May 2021 which amassed a total of 415 instances. Further, preprocessing of the dataset is executed with the use of normalization. The experimentation is executed through the use of four ensemble strategies which are bagging, boosting, stacking and voting with four distinct machine learning algorithms linear regression, sequential minimal optimizer for regression, multilayer perceptron and Gaussian process. The splitting of the dataset is completed at 75%, and machine learning algorithms with ensemble techniques are applied. Linear regression with the bagging ensemble method gives satisfactory outcomes with the correlation coefficient of 0.935 and 0.919 for confirmed cases and recovered cases, respectively, and Gaussian process presented the best results for deceased cases. In the case of ensemble strategies, bagging indicates the best correlation coefficient in each case. Therefore, with the help of the three best algorithms, confirmed cases, recovered cases and deceased cases predictions are performed. The paper has potential implementations that can foresee the COVID-19 confirmed cases, recovered cases and deceased cases based on historic data and subsequently structure the plan for the future.

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Notes

  1. 1.

    https://www.cs.waikato.ac.nz/ml/weka.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

    Nidhi Kumari Chauhan & Chhavi Goel

  2. Postdoctoral Fellow, Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil

    Pooja Singh

  3. Associate Professor, Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Knowledge Park-3, Greater Noida (U.P.), 201306, India

    Pooja Singh

Authors
  1. Nidhi Kumari Chauhan
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  2. Chhavi Goel
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  3. Pooja Singh
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Corresponding author

Correspondence to Nidhi Kumari Chauhan .

Editor information

Editors and Affiliations

  1. Sikkim Manipal Institute of Technology, Majitar, India

    Mousumi Gupta

  2. Institute of Engineering and Management, Kolkata, India

    Sujata Ghatak

  3. Sikkim Manipal Institute of Medical Sciences, Sikkim, India

    Amlan Gupta

  4. Henry Ford Hospital, Detroit, MI, USA

    Abir Lal Mukherjee

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Chauhan, N.K., Goel, C., Singh, P. (2023). Comparative Analysis of Machine Learning Algorithms with Ensemble Techniques and Forecasting COVID-19 Cases in India. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_6

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  • DOI: https://doi.org/10.1007/978-981-19-0151-5_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0150-8

  • Online ISBN: 978-981-19-0151-5

  • eBook Packages: EngineeringEngineering (R0)

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