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dc.contributor.authorRamírez, Javier
dc.contributor.authorGórriz-Sáez, Juan Manuel
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorMartínez-Murcia, Francisco Jesús
dc.contributor.authorSegovia, Fermín
dc.contributor.authorSalas-González, Diego
dc.contributor.authorCastillo-Barnes, Diego
dc.contributor.authorÁlvarez-Illán, Ignacio
dc.contributor.authorPuntonet, Carlos
dc.date.accessioned2023-11-21T11:51:52Z
dc.date.available2023-11-21T11:51:52Z
dc.date.issued2017-12-11
dc.identifier.citationRamírez, Javier & Gorriz, Juan & Ortiz, Andrés & Martínez-Murcia, F.J. & Segovia, F. & Salas-Gonzalez, Diego & Castillo-Barnes, Diego & Illan, Ignacio & Puntonet, Carlos. (2017). Ensemble of random forests One vs . Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares. Journal of Neuroscience Methods. 302. 10.1016/j.jneumeth.2017.12.005.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28097
dc.description.abstractBackground: Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10 % to 15 % per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. Method: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of one vs. rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. Results: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25 % classification score on the test subset which consists of 160 real subjects. Comparison with Existing Method(s): The classifier yielded the best performance when compared to: i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and iii) bagging ensemble learning. Conclusions: A robust method has been proposed for the international challenge on MCI prediction based on MRI data.es_ES
dc.description.sponsorshipThis work was supported by the MINECO/FEDER under TEC2015-64718-R project, the Consejería de Economía, Innovacion, Ciencia, y Empleo of the Junta de Andalucía under the P11-TIC-7103 Excellence Project and the Salvador de Madariaga Mobility Grants 2017.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAlzheimer, Enfermedad de - Diagnóstico por imagen - Proceso de datoses_ES
dc.subjectCerebro - Diagnóstico por imagen - Proceso de datoses_ES
dc.subjectDiagnóstico - Proceso de datoses_ES
dc.subjectMedicina - Proceso de datoses_ES
dc.subject.otherMagnetic resonance imaginges_ES
dc.subject.otherComputer-aided diagnosises_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherAlzheimer’s diseasees_ES
dc.subject.otherMild cognitive impairmentes_ES
dc.subject.otherRandom forestses_ES
dc.subject.otherBagginges_ES
dc.subject.otherANOVA feature selectiones_ES
dc.subject.otherOne vs. rest classificationes_ES
dc.titleEnsemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.jneumeth.2017.12.005
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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