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Impact of COVID-19 Pandemic on Diet Prediction and Patient Health Based on Support Vector Machine

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

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Abstract

Recently, the COVID-19 pandemic has an efficient impact on all things around the world. Food estimation or diet has been grown great attention in the recent pandemic. This paper utilizes the Support Vector Machine (SVM) to predict the effect of the COVID-19 pandemic on a diet and further forecast the number of persons subject to death due to this pandemic. This work is based on the available dataset that contains fat quantity, energy intake (kcal), food supply quantity (kg), and protein for different categories of food. Furthermore, we are concerned the animal products, cereals excluding beer, obesity, including vegetal products that affect humans’ general health during the pandemic.

Furthermore, the dataset includes confirmed deaths, recovered, and active cases in the percentage of each country’s current population. The results depend on Root Mean Square Error (RMSE), which indicates that SVM’s use with the Radial Basis Function (RBF) kernel produces 0.27. Further, SVM with linear Kernel achieves 0.18 RMSE, and finally, deep regression model achieves 0.29 RMSE.

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Author information

Authors and Affiliations

  1. Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh, 33511, Egypt

    M. Y. Shams

  2. Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

    O. M. Elzeki

  3. Misr Higher Institute for Commerce and Computers, MET, Mansoura, Egypt

    Mohamed Abd Elfattah & Lobna M. Abouelmagd

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Authors
  1. M. Y. Shams
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  2. O. M. Elzeki
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  3. Mohamed Abd Elfattah
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  4. Lobna M. Abouelmagd
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  5. Ashraf Darwish
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  6. Aboul Ella Hassanien
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Corresponding author

Correspondence to M. Y. Shams .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Shams, M.Y., Elzeki, O.M., Abd Elfattah, M., Abouelmagd, L.M., Darwish, A., Hassanien, A.E. (2021). Impact of COVID-19 Pandemic on Diet Prediction and Patient Health Based on Support Vector Machine. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_7

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