Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics

[EN] This paper introduces a methodology to build mathematical models based on evidence and data sets, considering data and model uncertainty. We study the evolution of obesity in the population, being obesity a consequence of the transmission of unhealthy lifestyle habits and behavioral patterns in...

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Autores: Parra, Daniel, Hidalgo, J. Ignacio, Velasco, J.M., Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/210277
Acceso en línea:https://riunet.upv.es/handle/10251/210277
Access Level:acceso abierto
Palabra clave:Datasets
Automatic model building
Dynamic models
Uncertainty quantification
MATEMATICA APLICADA
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spelling Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamicsParra, DanielHidalgo, J. IgnacioVelasco, J.M.Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532DatasetsAutomatic model buildingDynamic modelsUncertainty quantificationMATEMATICA APLICADA[EN] This paper introduces a methodology to build mathematical models based on evidence and data sets, considering data and model uncertainty. We study the evolution of obesity in the population, being obesity a consequence of the transmission of unhealthy lifestyle habits and behavioral patterns influenced by social networks (family, friends, peers, etc.). We propose a three-step methodology. First, we create a synthetic data set based on a previous model with real data. Then, we search for dynamic models based on difference equations that best fit the dynamics described by the dataset and their uncertainty (uncertainty-aware). To do this, we use a dynamic structured grammatical evolution algorithm (an algorithm that builds possible models) on which we have defined a grammar (set of possible expressions that can be part of the model). The definition of appropriate grammar is crucial because it allows us to build models that do not contradict the knowledge of the phenomenon studied. However, the data may suggest introducing new terms that indicate the influence of unknown factors. Finally, from among all the models obtained, we will algorithmically search for a selection of them that best describes the uncertainty of the data. This methodology can be applied to various scenarios with available datasets and a limited understanding of the phenomenon. It aims to generate models that not only achieve precision but also incorporate terms that correspond to identifiable processes, which can be explained within the context of the study problem.This work has been supported by the grants PID2020-115270GB-I00, PDC2022-133429-I00 and PID2021-125549OB-I00 funded by MCIN / AEI/ 10.13039 / 501100011033 and by European Union Next GenerationEU / PRTR.Springer-VerlagFacultad de Administración y Dirección de EmpresasDepartamento de Matemática AplicadaInstituto Universitario de Matemática MultidisciplinarEuropean CommissionAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-09-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/210277reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115270GB-I00 ECUACIONES DIFERENCIALES ALEATORIAS. CUANTIFICACION DE LA INCERTIDUMBRE Y APLICACIONESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PDC2022-133429-I00 SISTEMA WEARABLE DE INTELIGENCIA ARTIFICIAL PARA LA TOMA DE DECISIONES DE PERSONAS CON DIABETESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-125549OB-I00 INTELIGENCIA ARTIFICIAL SOBRE ACELERADORES HARDWARE ESPECIALIZADOS Y SISTEMAS EMPOTRADOS PARA EL TRATAMIENTO PERSONALIZADO DE PRECISION DE LA DIABETESopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2102772026-06-13T07:49:27Z
dc.title.none.fl_str_mv Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
title Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
spellingShingle Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
Parra, Daniel
Datasets
Automatic model building
Dynamic models
Uncertainty quantification
MATEMATICA APLICADA
title_short Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
title_full Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
title_fullStr Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
title_full_unstemmed Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
title_sort Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
dc.creator.none.fl_str_mv Parra, Daniel
Hidalgo, J. Ignacio
Velasco, J.M.
Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532
author Parra, Daniel
author_facet Parra, Daniel
Hidalgo, J. Ignacio
Velasco, J.M.
Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532
author_role author
author2 Hidalgo, J. Ignacio
Velasco, J.M.
Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532
author2_role author
author
author
dc.contributor.none.fl_str_mv Facultad de Administración y Dirección de Empresas
Departamento de Matemática Aplicada
Instituto Universitario de Matemática Multidisciplinar
European Commission
Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Datasets
Automatic model building
Dynamic models
Uncertainty quantification
MATEMATICA APLICADA
topic Datasets
Automatic model building
Dynamic models
Uncertainty quantification
MATEMATICA APLICADA
description [EN] This paper introduces a methodology to build mathematical models based on evidence and data sets, considering data and model uncertainty. We study the evolution of obesity in the population, being obesity a consequence of the transmission of unhealthy lifestyle habits and behavioral patterns influenced by social networks (family, friends, peers, etc.). We propose a three-step methodology. First, we create a synthetic data set based on a previous model with real data. Then, we search for dynamic models based on difference equations that best fit the dynamics described by the dataset and their uncertainty (uncertainty-aware). To do this, we use a dynamic structured grammatical evolution algorithm (an algorithm that builds possible models) on which we have defined a grammar (set of possible expressions that can be part of the model). The definition of appropriate grammar is crucial because it allows us to build models that do not contradict the knowledge of the phenomenon studied. However, the data may suggest introducing new terms that indicate the influence of unknown factors. Finally, from among all the models obtained, we will algorithmically search for a selection of them that best describes the uncertainty of the data. This methodology can be applied to various scenarios with available datasets and a limited understanding of the phenomenon. It aims to generate models that not only achieve precision but also incorporate terms that correspond to identifiable processes, which can be explained within the context of the study problem.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-09-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/210277
url https://riunet.upv.es/handle/10251/210277
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-115270GB-I00 ECUACIONES DIFERENCIALES ALEATORIAS. CUANTIFICACION DE LA INCERTIDUMBRE Y APLICACIONES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PDC2022-133429-I00 SISTEMA WEARABLE DE INTELIGENCIA ARTIFICIAL PARA LA TOMA DE DECISIONES DE PERSONAS CON DIABETES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-125549OB-I00 INTELIGENCIA ARTIFICIAL SOBRE ACELERADORES HARDWARE ESPECIALIZADOS Y SISTEMAS EMPOTRADOS PARA EL TRATAMIENTO PERSONALIZADO DE PRECISION DE LA DIABETES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer-Verlag
publisher.none.fl_str_mv Springer-Verlag
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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