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

Vista sencilla de ítem
dc.contributor.authorOrmeño, Pablo
dc.contributor.authorMárquez, Gastón
dc.contributor.authorGuerrero Nancuante, Camilo
dc.contributor.authorTaramasco, Carla
dc.date.accessioned2023-11-22T13:53:52Z
dc.date.available2023-11-22T13:53:52Z
dc.date.issued2022-07-01
dc.descriptionIndexación: Scopus.es
dc.description.abstractEpivigila 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, F1-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, F1-score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.es
dc.identifier.citationInternational Journal of Environmental Research and Public Health, Volume 19, Issue 13, July-1 2022, Article number 8058es
dc.identifier.issn1661-7827
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/54017
dc.language.isoenes
dc.publisherMDPIes
dc.subjectComorbiditieses
dc.subjectEpivigilaes
dc.subjectMachine learninges
dc.subjectSymptomses
dc.titleDetection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Studyes
dc.typeArtículoes
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Ormeño_Detection_of_COVID19_patients_using.pdf
Tamaño:
628.29 KB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLES
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FIng - Artículos de Revista