IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i13p8058-d852835.html
   My bibliography  Save this article

Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study

Author

Listed:
  • Pablo Ormeño

    (Escuela de Ingenieria y Negocios, Universidad de Viña del Mar, Viña del Mar 2520000, Chile)

  • Gastón Márquez

    (Departamento de Electrónica e Informática, Universidad Técnica Federico Santa María, Millennium Nucleus on Sociomedicine, Concepción 4030000, Chile)

  • Camilo Guerrero-Nancuante

    (Escuela de Enfermería, Universidad de Valparaíso, Valparaíso 2500000, Chile)

  • Carla Taramasco

    (Facultad de Ingeniería, Universidad Andrés Bello, Millennium Nucleus on Sociomedicine, Viña del Mar 2520000, Chile)

Abstract

Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F 1 -score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F 1 -score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.

Suggested Citation

  • Pablo Ormeño & Gastón Márquez & Camilo Guerrero-Nancuante & Carla Taramasco, 2022. "Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study," IJERPH, MDPI, vol. 19(13), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8058-:d:852835
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/13/8058/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/13/8058/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vageesh Jain & Jin-Min Yuan, 2020. "Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(5), pages 533-546, June.
    2. Ronald Manríquez & Camilo Guerrero-Nancuante & Felipe Martínez & Carla Taramasco, 2021. "Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19," IJERPH, MDPI, vol. 18(9), pages 1-25, April.
    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. Artem Kashtanov & Ekaterina Molotok & Andrey Yavorovskiy & Alexander Boyarkov & Yuriy Vasil’ev & Ali Alsaegh & Sergey Dydykin & Olesya Kytko & Renata Meylanova & Yulianna Enina & Vasiliy Troitskiy & M, 2022. "A Comparative Cross-Sectional Study Assessing the Psycho-Emotional State of Intensive Care Units’ Physicians and Nurses of COVID-19 Hospitals of a Russian Metropolis," IJERPH, MDPI, vol. 19(3), pages 1-22, February.
    2. Eliza Wasilewska & Agnieszka Sobierajska-Rek & Karolina Śledzińska & Sylwia Małgorzewicz & Ewa Jassem & Jolanta Wierzba, 2021. "Morbidity, Clinical Course and Vaccination against SARS-CoV-2 Virus in Patients with Duchenne Muscular Dystrophy: A Patient Reported Survey," IJERPH, MDPI, vol. 19(1), pages 1-10, December.
    3. Matthias Wolfgang Heinzl & Lisa Kolenchery & Michael Resl & Carmen Klammer & Anne Black & Florian Obendorf & Lukas Schinagl & Roland Feldbauer & Johannes Pohlhammer & Thomas Wagner & Thomas Berger & B, 2022. "High Anti-CoV2S Antibody Levels at Hospitalization Are Associated with Improved Survival in Patients with COVID-19 Vaccine Breakthrough Infection," IJERPH, MDPI, vol. 19(23), pages 1-9, November.
    4. Rosa Magallón-Botaya & Bárbara Oliván-Blázquez & Karen Lizzette Ramírez-Cervantes & Fátima Méndez-López-de-la-Manzanara & Isabel Aguilar-Palacio & Marc Casajuana-Closas & Eva Andrés-Esteban, 2021. "Geographic Factors Associated with Poorer Outcomes in Patients Diagnosed with COVID-19 in Primary Health Care," IJERPH, MDPI, vol. 18(7), pages 1-11, April.
    5. Michał Chojnicki & Agnieszka Neumann-Podczaska & Mikołaj Seostianin & Zofia Tomczak & Hamza Tariq & Jerzy Chudek & Sławomir Tobis & Iwona Mozer-Lisewska & Aleksandra Suwalska & Andrzej Tykarski & Piot, 2021. "Long-Term Survival of Older Patients Hospitalized for COVID-19. Do Clinical Characteristics upon Admission Matter?," IJERPH, MDPI, vol. 18(20), pages 1-16, October.
    6. Huilong Wang & Meimei Wang & Rong Yang & Huijuan Yang, 2023. "Urban Resilience of Important Node Cities in Population Migration under the Influence of COVID-19 Based on Mamdani Fuzzy Inference System," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    7. Jennifer Nazareno & Emily Yoshioka & Alexander C Adia & Arjee Restar & Don Operario & Catherine Ceniza Choy, 2021. "From imperialism to inpatient care: Work differences of Filipino and White registered nurses in the United States and implications for COVID‐19 through an intersectional lens," Gender, Work and Organization, Wiley Blackwell, vol. 28(4), pages 1426-1446, July.

    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:19:y:2022:i:13:p:8058-:d:852835. 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.