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Data-Driven Prediction of Foodborne Disease Pathogens

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Data Science (ICPCSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1451))

Abstract

In recent years, foodborne diseases have become one of the most Data analysis technology has been widely used in the field of public health, and greatly facilitates the preliminary judgment of medical staff. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases. However, foodborne diseases caused by different pathogens lack specificity in clinical features, and the actual clinical pathogen detection ratio is very low in reality. This paper proposes a data-driven foodborne disease pathogen prediction model, which paves the way for early and effective patient identification and treatment. Data analysis was implemented to model the foodborne disease case data. The best model achieves good classification accuracy for Salmonella, Norovirus, Vibrio parahaemolyticus, Staphylococcus aureus, Shigella and Escherichia coli. With the patient data input, the model can conduct rapid risk assessment. The experimental results show that the data-driven approach reduces manual intervention and the difficulty of testing.

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Authors and Affiliations

  1. Faculty of Computing, Harbin Institute of Technology, Harbin, China

    Xiang Chen & Hongzhi Wang

Authors
  1. Xiang Chen
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  2. Hongzhi Wang
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Corresponding author

Correspondence to Hongzhi Wang .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. North University of China, Taiyuan, China

    Pinle Qin

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Chen, X., Wang, H. (2021). Data-Driven Prediction of Foodborne Disease Pathogens. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_8

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  • DOI: https://doi.org/10.1007/978-981-16-5940-9_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5939-3

  • Online ISBN: 978-981-16-5940-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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