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Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

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  • Kavadi, Durga Prasad
  • Patan, Rizwan
  • Ramachandran, Manikandan
  • Gandomi, Amir H.

Abstract

The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.

Suggested Citation

  • Kavadi, Durga Prasad & Patan, Rizwan & Ramachandran, Manikandan & Gandomi, Amir H., 2020. "Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304537
    DOI: 10.1016/j.chaos.2020.110056
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    References listed on IDEAS

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    1. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
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    Cited by:

    1. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Amir Masoud Rahmani & Seyedeh Yasaman Hosseini Mirmahaleh, 2024. "An Intelligent Algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1001-1020, March.
    4. Farhana Tazmim Pinki & Md Abdul Awal & Khondoker Mirazul Mumenin & Md. Shahadat Hossain & Jabed Al Faysal & Rajib Rana & Latifah Almuqren & Amel Ksibi & Md Abdus Samad, 2023. "HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting," Mathematics, MDPI, vol. 11(18), pages 1-19, September.

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