A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

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dc.contributor.authorFlores, Christopher
dc.contributor.authorTaramasco, Carla
dc.contributor.authorLagos, Maria Elena
dc.contributor.authorRimassa, Carla
dc.contributor.authorFigueroa, Rosa
dc.date.accessioned2022-11-30T02:46:20Z
dc.date.available2022-11-30T02:46:20Z
dc.date.issued2021
dc.description.abstractThe 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameFlores_Fea2021.pdf
dc.identifier.citationFlores, C.; Taramasco, C.; Lagos, M.E.; Rimassa, C.; Figueroa, R. A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study. Appl. Sci. 2021, 11, 7080. https://doi.org/10.3390/app11157080en_ES
dc.identifier.doihttps://doi.org/10.3390/app11157080
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7326
dc.languageen
dc.publisherMDPI
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.sourceApplied Sciences
dc.subjectCOVID-19en_ES
dc.subjectINCIDENCEen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.subjectSARS-COV-2en_ES
dc.subjectTIME SERIES CLASSIFICATIONen_ES
dc.titleA Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica

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