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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/214993
Ver los metadatos del registro completo
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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/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
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Budapest Tech |
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Budapest Tech |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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