dor_id: 4136225

506.#.#.a: Público

590.#.#.d: El proceso de revisión por pares (doble-ciego), cuenta con la participación de investigadores nacionales e internacionales de alto nivel y probada calidad científica y metodológica

510.0.#.a: Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Dialnet; Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); SCOPUS; Scientific Electronic Library Online (SciELO); Consejo Nacional de Ciencia y Tecnología (CONACYT)

561.#.#.u: http://www.iztacala.unam.mx/

650.#.4.x: Medicina y Ciencias de la Salud

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://journals.iztacala.unam.mx/index.php/amta/index

351.#.#.b: Revista Mexicana de Trastornos Alimentarios

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://journals.iztacala.unam.mx/index.php/amta/article/view/718/839

100.1.#.a: Aguilera Sosa, Víctor Ricardo; Méndez, Bárbara Itzel; Murillo, María Magdalena; Pérez Vielma, Nadia Mabel; Leija Alva, Gerardo; Montufar Burgos, Itzihuari Iratzi; Alvarado García, Angélica Serena; Duran Arciniega, Roxana Sarai

524.#.#.a: Aguilera Sosa, Víctor Ricardo, et al. (2022). Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults. Revista Mexicana de Trastornos Alimentarios; Vol. 12, Núm. 1, 2022; 61-70. Recuperado de https://repositorio.unam.mx/contenidos/4136225

245.1.0.a: Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Estudios Superiores Iztacala, UNAM

264.#.0.c: 2022

264.#.1.c: 2022-07-01

653.#.#.a: healthy habits; neuropsychological variables; body fat; artificial neural networks

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editorrmta@campus.iztacala.unam.mx

884.#.#.k: https://journals.iztacala.unam.mx/index.php/amta/article/view/718

001.#.#.#: 121.oai:ojs.pkp.sfu.ca:article/718

041.#.7.h: eng

520.3.#.a: Background There is a growing interest to understand the neural functions and substrates of complex cognitive processes related to Obesity (OB). Artificial Intelligence (AI) is being applied, specifically the perceptron model of Artificial Neural Networks (ANN) in non-communicable chronic diseases, to identify with greater certainty the connective factors (synaptic networks) between the input variables and the output variables associated. Objective Identify the synaptic weights of the ANN whose input variables are the executive functions (EF) and healthy lifestyles as predictors of Body Fat Percentage (BFP) in a group of adult subjects with different levels of BFP. Methods The Neuropsychological Battery (BANFE-2) and the Overeating Questionnaire (OQ) were administered to 40 participants aged between 18 and 38 years. BFP was measured using a RENPHO ES-24M Smart Body Composition Scale. The perceptron ANN model with ten trials was applied with a multilayer-perceptron. Results The ANN showed that the sensory variables with greater synaptic weight for BFP were Stroop A and B Errors and Successes of BANFE-2, and OQ scales Rationalizations and Healthy Habits. Conclusions ANN proved to be important in the simultaneous analysis of neuropsychological and healthy lifestyle data for the analysis of OB prevention and treatment by identifying the variables that are closely related.

773.1.#.t: Revista Mexicana de Trastornos Alimentarios; Vol. 12, Núm. 1 (2022): ENERO -JUNIO; 61-70

773.1.#.o: https://journals.iztacala.unam.mx/index.php/amta/index

022.#.#.a: ISSN impreso: 2007-1523

310.#.#.a: Semestral

300.#.#.a: Páginas: 61-70

264.#.1.b: Facultad de Estudios Superiores Iztacala, UNAM

doi: https://doi.org/10.22201/fesi.20071523e.2022.1.718

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2022-07-01 17:51:12.0

file_modification_date: 2022-07-01 17:51:13.0

file_name: 36d264f42095648284b6df6ccd2c6fddaa1024b3d8bc7af4abac0354a6582e8d.pdf

file_pages_number: 10

file_format_version: application/pdf; version=1.4

file_size: 515928

last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es

license_type: by-nc-nd

No entro en nada

No entro en nada 2

Artículo

Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults

Aguilera Sosa, Víctor Ricardo; Méndez, Bárbara Itzel; Murillo, María Magdalena; Pérez Vielma, Nadia Mabel; Leija Alva, Gerardo; Montufar Burgos, Itzihuari Iratzi; Alvarado García, Angélica Serena; Duran Arciniega, Roxana Sarai

Facultad de Estudios Superiores Iztacala, UNAM, publicado en Revista Mexicana de Trastornos Alimentarios, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Aguilera Sosa, Víctor Ricardo, et al. (2022). Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults. Revista Mexicana de Trastornos Alimentarios; Vol. 12, Núm. 1, 2022; 61-70. Recuperado de https://repositorio.unam.mx/contenidos/4136225

Descripción del recurso

Autor(es)
Aguilera Sosa, Víctor Ricardo; Méndez, Bárbara Itzel; Murillo, María Magdalena; Pérez Vielma, Nadia Mabel; Leija Alva, Gerardo; Montufar Burgos, Itzihuari Iratzi; Alvarado García, Angélica Serena; Duran Arciniega, Roxana Sarai
Tipo
Artículo de Investigación
Área del conocimiento
Medicina y Ciencias de la Salud
Título
Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults
Fecha
2022-07-01
Resumen
Background There is a growing interest to understand the neural functions and substrates of complex cognitive processes related to Obesity (OB). Artificial Intelligence (AI) is being applied, specifically the perceptron model of Artificial Neural Networks (ANN) in non-communicable chronic diseases, to identify with greater certainty the connective factors (synaptic networks) between the input variables and the output variables associated. Objective Identify the synaptic weights of the ANN whose input variables are the executive functions (EF) and healthy lifestyles as predictors of Body Fat Percentage (BFP) in a group of adult subjects with different levels of BFP. Methods The Neuropsychological Battery (BANFE-2) and the Overeating Questionnaire (OQ) were administered to 40 participants aged between 18 and 38 years. BFP was measured using a RENPHO ES-24M Smart Body Composition Scale. The perceptron ANN model with ten trials was applied with a multilayer-perceptron. Results The ANN showed that the sensory variables with greater synaptic weight for BFP were Stroop A and B Errors and Successes of BANFE-2, and OQ scales Rationalizations and Healthy Habits. Conclusions ANN proved to be important in the simultaneous analysis of neuropsychological and healthy lifestyle data for the analysis of OB prevention and treatment by identifying the variables that are closely related.
Tema
healthy habits; neuropsychological variables; body fat; artificial neural networks
Idioma
eng
ISSN
ISSN impreso: 2007-1523

Enlaces