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dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorGalar Idoate, Mikeles_ES
dc.creatorJurío Munárriz, Aránzazues_ES
dc.creatorBrugos Larumbe, Antonioes_ES
dc.creatorPagola Barrio, Migueles_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.description.abstractObjective: To develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next ten years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system. Methods: Linguistic fuzzy rule-based classification systems are used, since they provide a good classification rate and a highly interpretable model. More specifically, a new methodology to combine fuzzy rule-based classification systems with interval-valued fuzzy sets is proposed, which is composed of three steps: 1) the modelling of the linguistic labels of the classifier using interval-valued fuzzy sets; 2) the use of the Kα operator in the inference process and 3) the application of a genetic tuning to find the best ignorance degree that each interval-valued fuzzy set represents as well as the best value for the parameter α of the Kα operator in each rule. Results: The suitability of the new proposal to deal with this medical diagnosis classification problem is shown by comparing its performance with respect to the one provided by two classical fuzzy classifiers and a previous interval-valued fuzzy rule-based classification system. The performance of the new method is statistically better than the ones obtained with the methods considered in the comparison. The new proposal enhances both the total number of correctly diagnosed patients, around 3% with respect the classical fuzzy classifiers and around 1% versus the previous interval-valued fuzzy classifier, and the classifier ability to correctly differentiate patients of the different risk categories. Conclusion: The proposed methodology is a suitable tool to face the medical diagnosis of cardiovascular diseases, since it obtains a good classification rate and it also provides an interpretable model that can be easily understood by the doctors.en
dc.description.sponsorshipThis work was partially supported by the Spanish Ministry of Science and Technology under project TIN2010-15055 and the Research Services of the Universidad Pública de Navarra.en
dc.relation.ispartofApplied Soft Computing 20 (2014) 103–111en
dc.rights© 2013 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 licenseen
dc.subjectLinguistic fuzzy rule-based classification systemsen
dc.subjectInterval-valued fuzzy setsen
dc.subjectGenetic fuzzy systemsen
dc.subjectCardiovascular diseasesen
dc.titleMedical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification systemen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentAutomática y Computaciónes_ES
dc.contributor.departmentAutomatika eta Konputazioaeu
dc.contributor.departmentCiencias de la Saludes_ES
dc.contributor.departmentOsasun Zientziakeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes

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© 2013 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license
Except where otherwise noted, this item's license is described as © 2013 Elsevier B.V. The manuscript version is made available under the CC BY-NC-ND 4.0 license

El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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