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Autordc.contributor.authorPeñafiel, Sergio 
Autordc.contributor.authorBaloian Tataryan, Nelson 
Autordc.contributor.authorSanson, Horacio 
Autordc.contributor.authorPino Urtubia, José 
Fecha ingresodc.date.accessioned2021-09-22T16:34:04Z
Fecha disponibledc.date.available2021-09-22T16:34:04Z
Fecha de publicacióndc.date.issued2021
Cita de ítemdc.identifier.citationIEEE Access (2021) Volumen9 Página1154-1166es_ES
Identificadordc.identifier.other10.1109/ACCESS.2020.3047195
Identificadordc.identifier.urihttps://repositorio.uchile.cl/handle/2250/182063
Resumendc.description.abstractPredicting an individual's risk of getting a stroke has been a research subject for many authors worldwide since it is a frequent illness and there is strong evidence that early awareness of having that risk can be bene cial for prevention and treatment. Many Governments have been collecting medical data about their own population with the purpose of using arti cial intelligence methods for making those predictions. The most accurate ones are based on so called black-box methods which give little or no information about why they make a certain prediction. However, in the medical eld the explanations are sometimes more important than the accuracy since they allow specialists to gain insight about the factors that in uence the risk level. It is also frequent to nd medical information records with some missing data. In this work, we present the development of a prediction method which not only outperforms some other existing ones but it also gives information about the most probable causes of a high stroke risk and can deal with incomplete data records. It is based on the Dempster-Shafer theory of plausibility. For the testing we used data provided by the regional hospital in Okayama, Japan, a country in which people are compelled to undergo annual health checkups by law. This article presents experiments comparing the results of the Dempster-Shafer method with the ones obtained using other well-known machine learning methods like Multilayer perceptron, Support Vector Machines and Naive Bayes. Our approach performed the best in these experiments with some missing data. It also presents an analysis of the interpretation of rules produced by the method for doing the classi cation. The rules were validated by both medical literature and human specialists.es_ES
Patrocinadordc.description.sponsorshipConicyt (Chile) Master Scholarship 22180506es_ES
Idiomadc.language.isoenes_ES
Publicadordc.publisherIEEE-Inst Electrical Electronics Engineerses_ES
Tipo de licenciadc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link a Licenciadc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Fuentedc.sourceIEEE Accesses_ES
Palabras clavesdc.subjectData modelses_ES
Palabras clavesdc.subjectPredictive modelses_ES
Palabras clavesdc.subjectStroke (medical condition)es_ES
Palabras clavesdc.subjectMachine learninges_ES
Palabras clavesdc.subjectComputational modelinges_ES
Palabras clavesdc.subjectBayes methodses_ES
Palabras clavesdc.subjectMedical diagnostic imaginges_ES
Palabras clavesdc.subjectDempster-Shafer theoryes_ES
Palabras clavesdc.subjectStrokees_ES
Palabras clavesdc.subjectExpert systemses_ES
Palabras clavesdc.subjectInterpretable classificationes_ES
Títulodc.titlePredicting stroke risk with an interpretable classifieres_ES
Tipo de documentodc.typeArtículo de revista
Catalogadoruchile.catalogadorcfres_ES
Indizaciónuchile.indexArtículo de publicación ISIes_ES


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivs 3.0 Chile