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

Ormeño, Pablo; Márquez, Gastón; Guerrero-Nancuante, Camilo; Taramasco, Carla

doi:10.3390/ijerph19138058

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

Pablo Ormeño

^1,*

Gastón Márquez

^2,*

Camilo Guerrero-Nancuante

^3,*

and

Carla Taramasco

^4,*

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

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

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

⁴

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

Authors to whom correspondence should be addressed.

Academic Editors: Kathleen Tori and Carey Mather

Int. J. Environ. Res. Public Health 2022, 19(13), 8058; https://doi.org/10.3390/ijerph19138058

Received: 7 May 2022 / Revised: 23 June 2022 / Accepted: 23 June 2022 / Published: 30 June 2022

(This article belongs to the Special Issue Digital Health and Point of Care: Contributions to the ‘New Normal’ of Healthcare Provision)

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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. View Full-Text

Keywords: Epivigila; machine learning; symptoms; comorbidities

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MDPI and ACS Style

Ormeño, P.; Márquez, G.; Guerrero-Nancuante, C.; Taramasco, C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. Int. J. Environ. Res. Public Health 2022, 19, 8058. https://doi.org/10.3390/ijerph19138058

AMA Style

Ormeño P, Márquez G, Guerrero-Nancuante C, Taramasco C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. International Journal of Environmental Research and Public Health. 2022; 19(13):8058. https://doi.org/10.3390/ijerph19138058

Chicago/Turabian Style

Ormeño, Pablo, Gastón Márquez, Camilo Guerrero-Nancuante, and Carla Taramasco. 2022. "Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study" International Journal of Environmental Research and Public Health 19, no. 13: 8058. https://doi.org/10.3390/ijerph19138058

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