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Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction

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Proceedings of International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 176))

Abstract

A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same datasets. Based on the simulation results and conducted comparative analysis, it is observed that the proposed hybrid approach has outperformed other sophisticated approaches and that it can be used as a tool for other time-series prediction.

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Author information

Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Miodrag Zivkovic, Nebojsa Bacanin, Aleksandar Djordjevic, Milos Antonijevic & Ivana Strumberger

  2. School of computer science and Engineering, VIT Bhopal University, Bhopal, India

    Venkatachalam K

  3. Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, KRG, Iraq

    Tarik A. Rashid

Authors
  1. Miodrag Zivkovic
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  2. Venkatachalam K
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  3. Nebojsa Bacanin
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  4. Aleksandar Djordjevic
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  5. Milos Antonijevic
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  6. Ivana Strumberger
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  7. Tarik A. Rashid
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Corresponding author

Correspondence to Miodrag Zivkovic .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

  4. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

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Zivkovic, M. et al. (2021). Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction. In: Shakya, S., Balas, V.E., Haoxiang, W., Baig, Z. (eds) Proceedings of International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol 176. Springer, Singapore. https://doi.org/10.1007/978-981-33-4355-9_14

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  • DOI: https://doi.org/10.1007/978-981-33-4355-9_14

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