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Data-influence analytics in predictive models applied to asthma disease

dc.contributor.authorTapia, Alejandra
dc.contributor.authorGiampaoli, Viviana
dc.contributor.authorLeiva, Víctor
dc.contributor.authorLio, Yuhlong
dc.date.accessioned2021-10-27T19:18:41Z
dc.date.available2021-10-27T19:18:41Z
dc.date.issued2020
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/3426
dc.description.abstractAsthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceMathematics, 8(9), 1587es_CL
dc.subjectBinary dataes_CL
dc.subjectFixed airway obstructiones_CL
dc.subjectMonte Carlo methodes_CL
dc.subjectMetropolis-Hastings methodes_CL
dc.subjectLocal influence diagnosticses_CL
dc.subjectGlobal influence diagnosticses_CL
dc.subjectMixed-effects logistic regressiones_CL
dc.subjectR softwarees_CL
dc.titleData-influence analytics in predictive models applied to asthma diseasees_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias Básicases_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionOtroes_CL
dc.ucm.uriwww.mdpi.com/2227-7390/8/9/1587es_CL
dc.ucm.doidoi.org/10.3390/math8091587es_CL


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