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dc.creatorViloria, Amelec
dc.creatorRodríguez López, Jorge
dc.creatorOrellano Llinás, Nataly
dc.creatorVargas Mercado, Carlos
dc.creatorLeón Coronado, Luz Estela
dc.creatorNegrete Sepulveda, Ana María
dc.creatorPineda, Omar
dc.date.accessioned2021-01-28T12:57:12Z
dc.date.available2021-01-28T12:57:12Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7783
dc.description.abstractIntegrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are not yet explored and that contribute to strategic decision-making processes, such as machine learning techniques or machine learning. In occupational safety and health management systems, it is important to carry out the proper follow-ups and process controls in any type of industry and organization whose relationship is direct. This paper presents the application of three methods related to data mining: Support Vector Machine algorithms, Naïve Bayes, and Genetic Algorithms to identify the degree of psychosocial risk in university teachers of the Mumbai University in India. The use of SVM easily recognizes physiological variables and the best prediction performance was achieved with 96.34% accuracy efficiency.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceLecture Notes in Electrical Engineeringspa
dc.subjectSupport vector machinespa
dc.subjectNaïve bayesspa
dc.subjectGenetic algorithmsspa
dc.titlePrediction of psychosocial risks in teachers using data miningspa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_50spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-3125-5_50


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