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dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2024-04-08T14:36:41Z
dc.date.available2024-04-08T14:36:41Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/20.500.13067/3105
dc.description.abstractThis paper presents a scheme of self-management that employs directly the theorem of Bayes to calculate realistic probabilities to experience stroke in the shortest and middle terms. In concrete the probabilities might be used in an application by which diabetic patients can carry out by themselves periodical measurements of probabilities of risk. It is emphazised the fact that Bayes rule can be powerful but used as tool to predict stroke might also to trigger false alarms or be blind to stroke.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherIEEEes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.subjectStrokees_PE
dc.subjectDiabeteses_PE
dc.subjectBayes theoremes_PE
dc.subjectCholesteroles_PE
dc.titleSelf-Management to Anticipate Stroke in Diabetic Patients Through Algorithm Based on Probability of Bayeses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)es_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.relation.urlhttps://doi.org/10.1109/ICECET58911.2023.10389192es_PE


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