IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v21y2024i9p1164-d1469103.html
   My bibliography  Save this article

Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil

Author

Listed:
  • Julliana Gonçalves Marques

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Bruno Motta de Carvalho

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Luiz Affonso Guedes

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Márjory Da Costa-Abreu

    (Department of Computing, Sheffield Hallam University, Sheffield S9 3TY, UK)

Abstract

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Suggested Citation

  • Julliana Gonçalves Marques & Bruno Motta de Carvalho & Luiz Affonso Guedes & Márjory Da Costa-Abreu, 2024. "Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil," IJERPH, MDPI, vol. 21(9), pages 1-15, September.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:9:p:1164-:d:1469103
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/21/9/1164/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/21/9/1164/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Margherita Macera & Giulia De Angelis & Caterina Sagnelli & Nicola Coppola & Vanvitelli COVID-19 Group, 2020. "Clinical Presentation of COVID-19: Case Series and Review of the Literature," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alberto Grassi & Luca Andriolo & Davide Golinelli & Dario Tedesco & Simona Rosa & Pasquale Gramegna & Jacopo Ciaffi & Riccardo Meliconi & Maria Paola Landini & Giuseppe Filardo & Maria Pia Fantini & S, 2021. "Higher 90-Day Mortality after Surgery for Hip Fractures in Patients with COVID-19: A Case–Control Study from a Single Center in Italy," IJERPH, MDPI, vol. 18(10), pages 1-13, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:21:y:2024:i:9:p:1164-:d:1469103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.