Machine Learning for Detection of Cognitive Impairment

Autores
Diaz, Valeria; Rodríguez, Guillermo Horacio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm.
Fil: Diaz, Valeria. Universidad de Palermo. Facultad de Ingeniería; Argentina
Fil: Rodríguez, Guillermo Horacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
MACHINE LEARNING
ALZHEIMER DISEASE
IMPAIRMENT
MILD COGNITIVE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214993

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spelling Machine Learning for Detection of Cognitive ImpairmentDiaz, ValeriaRodríguez, Guillermo HoracioMACHINE LEARNINGALZHEIMER DISEASEIMPAIRMENTMILD COGNITIVEhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm.Fil: Diaz, Valeria. Universidad de Palermo. Facultad de Ingeniería; ArgentinaFil: Rodríguez, Guillermo Horacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaBudapest Tech2022-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214993Diaz, Valeria; Rodríguez, Guillermo Horacio; Machine Learning for Detection of Cognitive Impairment; Budapest Tech; Acta Polytechnica Hungarica; 19; 5; 3-2022; 195-2131785-8860CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://acta.uni-obuda.hu/Issue123.htminfo:eu-repo/semantics/altIdentifier/url/http://acta.uni-obuda.hu/Diaz_Rodriguez_123.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:45:23Zoai:ri.conicet.gov.ar:11336/214993instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:45:23.473CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Machine Learning for Detection of Cognitive Impairment
title Machine Learning for Detection of Cognitive Impairment
spellingShingle Machine Learning for Detection of Cognitive Impairment
Diaz, Valeria
MACHINE LEARNING
ALZHEIMER DISEASE
IMPAIRMENT
MILD COGNITIVE
title_short Machine Learning for Detection of Cognitive Impairment
title_full Machine Learning for Detection of Cognitive Impairment
title_fullStr Machine Learning for Detection of Cognitive Impairment
title_full_unstemmed Machine Learning for Detection of Cognitive Impairment
title_sort Machine Learning for Detection of Cognitive Impairment
dc.creator.none.fl_str_mv Diaz, Valeria
Rodríguez, Guillermo Horacio
author Diaz, Valeria
author_facet Diaz, Valeria
Rodríguez, Guillermo Horacio
author_role author
author2 Rodríguez, Guillermo Horacio
author2_role author
dc.subject.none.fl_str_mv MACHINE LEARNING
ALZHEIMER DISEASE
IMPAIRMENT
MILD COGNITIVE
topic MACHINE LEARNING
ALZHEIMER DISEASE
IMPAIRMENT
MILD COGNITIVE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm.
Fil: Diaz, Valeria. Universidad de Palermo. Facultad de Ingeniería; Argentina
Fil: Rodríguez, Guillermo Horacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm.
publishDate 2022
dc.date.none.fl_str_mv 2022-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/214993
Diaz, Valeria; Rodríguez, Guillermo Horacio; Machine Learning for Detection of Cognitive Impairment; Budapest Tech; Acta Polytechnica Hungarica; 19; 5; 3-2022; 195-213
1785-8860
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214993
identifier_str_mv Diaz, Valeria; Rodríguez, Guillermo Horacio; Machine Learning for Detection of Cognitive Impairment; Budapest Tech; Acta Polytechnica Hungarica; 19; 5; 3-2022; 195-213
1785-8860
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://acta.uni-obuda.hu/Issue123.htm
info:eu-repo/semantics/altIdentifier/url/http://acta.uni-obuda.hu/Diaz_Rodriguez_123.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Budapest Tech
publisher.none.fl_str_mv Budapest Tech
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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