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A machine learning approach as an aid for early covid-19 detection
dc.contributor.author | Martinez-Velazquez R | |
dc.contributor.author | Tobón V. D.P | |
dc.contributor.author | Sanchez A | |
dc.contributor.author | El Saddik A | |
dc.contributor.author | Petriu E. | |
dc.date.accessioned | 2022-09-14T14:33:26Z | |
dc.date.available | 2022-09-14T14:33:26Z | |
dc.date.created | 2021 | |
dc.identifier.issn | 14248220 | |
dc.identifier.uri | http://hdl.handle.net/11407/7364 | |
dc.description | The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | eng |
dc.language.iso | eng | |
dc.publisher | MDPI AG | |
dc.relation.isversionof | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108064294&doi=10.3390%2fs21124202&partnerID=40&md5=3e8e58ec60b9e51af0ec3cb1c8ebea2e | |
dc.source | Sensors | |
dc.title | A machine learning approach as an aid for early covid-19 detection | |
dc.type | Article | |
dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | |
dc.publisher.program | Ingeniería de Telecomunicaciones | |
dc.type.spa | Artículo | |
dc.identifier.doi | 10.3390/s21124202 | |
dc.subject.keyword | COVID-19 detection | eng |
dc.subject.keyword | Machine learning | eng |
dc.subject.keyword | SARS-CoV-2 | eng |
dc.subject.keyword | Symptoms-based test | eng |
dc.subject.keyword | Developing countries | eng |
dc.subject.keyword | Diagnosis | eng |
dc.subject.keyword | Diseases | eng |
dc.subject.keyword | Turing machines | eng |
dc.subject.keyword | Viruses | eng |
dc.subject.keyword | Area under the curves | eng |
dc.subject.keyword | Best model | eng |
dc.subject.keyword | Coronaviruses | eng |
dc.subject.keyword | Machine learning approaches | eng |
dc.subject.keyword | Non essential | eng |
dc.subject.keyword | Physical interactions | eng |
dc.subject.keyword | Receiver operating characteristics | eng |
dc.subject.keyword | Research efforts | eng |
dc.subject.keyword | Machine learning | eng |
dc.relation.citationvolume | 21 | |
dc.relation.citationissue | 12 | |
dc.publisher.faculty | Facultad de Ingenierías | |
dc.affiliation | Martinez-Velazquez, R., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada | |
dc.affiliation | Tobón V., D.P., Faculty of Engineering, Universidad de Medellín, Carrera 87 No. 30-65, Medellin, 050010, Colombia | |
dc.affiliation | Sanchez, A., Department of Information Technology, University of Colima, Avenida Universidad 333, Las Viboras, Colima, Col., 28040, Mexico | |
dc.affiliation | El Saddik, A., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada | |
dc.affiliation | Petriu, E., School of Electrical Engineering and Computer Science, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON K1N 6N5, Canada | |
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dc.type.coar | http://purl.org/coar/resource_type/c_6501 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | |
dc.type.driver | info:eu-repo/semantics/article | |
dc.identifier.reponame | reponame:Repositorio Institucional Universidad de Medellín | |
dc.identifier.repourl | repourl:https://repository.udem.edu.co/ | |
dc.identifier.instname | instname:Universidad de Medellín |
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