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dc.creatorVíctor Manuel Treviño Alvarado
dc.creatorJosé Gerardo Tamez Peña
dc.date2015
dc.date.accessioned2018-10-18T21:51:10Z
dc.date.available2018-10-18T21:51:10Z
dc.identifier.issn23146133
dc.identifier.doi10.1155/2015/961314
dc.identifier.urihttp://hdl.handle.net/11285/630524
dc.descriptionThe early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD. © 2015 Antonio Martínez-Torteya et al.
dc.languageeng
dc.publisherHindawi Publishing Corporation
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84935861155&doi=10.1155%2f2015%2f961314&partnerID=40&md5=6e3ff20a2368506786648c084aa71461
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceBioMed Research International
dc.subjectapolipoprotein D
dc.subjectbiological marker
dc.subjectcomplement component C3
dc.subjectmonocyte chemotactic protein 4
dc.subjecttau protein
dc.subjectbiological marker
dc.subjectaged
dc.subjectAlzheimer disease
dc.subjectangular gyrus
dc.subjectArticle
dc.subjectcalibration
dc.subjectcase control study
dc.subjectcerebrospinal fluid analysis
dc.subjectclassifier
dc.subjectclinical feature
dc.subjectcontrolled study
dc.subjectdata base
dc.subjectdata mining
dc.subjectdiagnostic accuracy
dc.subjectdiagnostic test accuracy study
dc.subjectdiagnostic value
dc.subjectdisease classification
dc.subjecterythrocyte count
dc.subjectfemale
dc.subjectgenetic algorithm
dc.subjecthippocampus
dc.subjecthuman
dc.subjectlateral orbitofrontal cortex
dc.subjectmajor clinical study
dc.subjectmale
dc.subjectmedial orbitofrontal cortex
dc.subjectmedical history
dc.subjectmethodology
dc.subjectmiddle temporal gyrus
dc.subjectmild cognitive impairment
dc.subjectnuclear magnetic resonance imaging
dc.subjectpositron emission tomography
dc.subjectprotein blood level
dc.subjectsensitivity and specificity
dc.subjectsuperior frontal gyrus
dc.subjecttemporal lobe
dc.subjectvoxel based morphometry
dc.subjectAlzheimer disease
dc.subjectcognitive defect
dc.subjectdiagnostic imaging
dc.subjectearly diagnosis
dc.subjectfactual database
dc.subjectgenetics
dc.subjectmultimodal imaging
dc.subjectpathology
dc.subjectradiography
dc.subjectAlzheimer Disease
dc.subjectBiomarkers
dc.subjectCognitive Dysfunction
dc.subjectDatabases, Factual
dc.subjectEarly Diagnosis
dc.subjectHumans
dc.subjectMagnetic Resonance Imaging
dc.subjectMultimodal Imaging
dc.subjectRadiography
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleImproved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
dc.typeArtículo
dc.identifier.volume2015
refterms.dateFOA2018-10-18T21:51:10Z


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