dc.creatorHerrera-Huisa, Luis
dc.creatorArias-Meza, Nicole
dc.creatorCabanillas-Carbonell, Michael
dc.date.accessioned2022-03-10T20:08:03Z
dc.date.accessioned2023-05-30T23:14:31Z
dc.date.available2022-03-10T20:08:03Z
dc.date.available2023-05-30T23:14:31Z
dc.date.created2022-03-10T20:08:03Z
dc.date.issued2021-12-22
dc.identifierHerrera-Huisa, L., Arias-Meza, N. & Cabanillas-Carbonell, M. (2021, September). Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. In 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) (pp. 769-775). IEEE.
dc.identifier978-1-6654-3574-1
dc.identifierhttps://hdl.handle.net/20.500.13067/1755
dc.identifier2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
dc.identifierhttps://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6474146
dc.description.abstractThe world is currently experiencing a major pandemic with the SARS-CoV-2 virus in which many patients who suffer and have suffered from this disease are more likely to suffer from hypertension. For this purpose, we have carried out a review of the scientific literature, from which we have collected 105 articles obtained from the following databases: ProQuest, Dialnet, ScienceDirect, Scopus, IEEE Xplore. Subsequently, based on the inclusion and exclusion criteria, 68 articles were systematized, detailing that Machine Learning helps us in the detection and prediction of hypertension in patients with coronavirus, Likewise, the predictive models that allow better detection of hypertension in patients with Covid 19 are “Neural Networks”, “Cox Risk Model”, “Random Forest” and “XGBoost”, detailing the countries and technologies used.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisherPE
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124154461&doi=10.1109%2fISPA-BDCloud-SocialCom-SustainCom52081.202
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceAUTONOMA
dc.source769
dc.source775
dc.subjectHypertension
dc.subjectCOVID-19
dc.subjectSystematics
dc.subjectPandemics
dc.subjectDatabases
dc.subjectNeural networks
dc.subjectAsia
dc.titleAnalysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature
dc.typeinfo:eu-repo/semantics/article


Este ítem pertenece a la siguiente institución