Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamics
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 influen...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/117441 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/117441 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004 Datasets Automatic model building Dynamic models Uncertainty quantification Informática (Informática) 12 Matemáticas |
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Building uncertainty-aware mathematical models based on evidence from datasets using grammatical evolution optimization techniques: the case of the obesity dynamicsParra Rodríguez, DanielHidalgo Pérez, José IgnacioVelasco Cabo, José ManuelVillanueva Micó, Rafael Jacinto004DatasetsAutomatic model buildingDynamic modelsUncertainty quantificationInformática (Informática)12 MatemáticasThis 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.SpringerUniversidad Complutense de Madrid20242024-01-0120242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/117441reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengPID2020-115270GB-I00 Not available Not availablePDC2022-133429- I00 Not available Not availablePID2021-125549OB-I00 Not available Not availableMCIN AEI 10.13039open accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1174412026-06-02T12:44:21Z |
| 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 Rodríguez, Daniel 004 Datasets Automatic model building Dynamic models Uncertainty quantification Informática (Informática) 12 Matemáticas |
| 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 Rodríguez, Daniel Hidalgo Pérez, José Ignacio Velasco Cabo, José Manuel Villanueva Micó, Rafael Jacinto |
| author |
Parra Rodríguez, Daniel |
| author_facet |
Parra Rodríguez, Daniel Hidalgo Pérez, José Ignacio Velasco Cabo, José Manuel Villanueva Micó, Rafael Jacinto |
| author_role |
author |
| author2 |
Hidalgo Pérez, José Ignacio Velasco Cabo, José Manuel Villanueva Micó, Rafael Jacinto |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
Universidad Complutense de Madrid |
| dc.subject.none.fl_str_mv |
004 Datasets Automatic model building Dynamic models Uncertainty quantification Informática (Informática) 12 Matemáticas |
| topic |
004 Datasets Automatic model building Dynamic models Uncertainty quantification Informática (Informática) 12 Matemáticas |
| description |
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 2024-01-01 2024 2024-01-01 |
| 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://hdl.handle.net/20.500.14352/117441 |
| url |
https://hdl.handle.net/20.500.14352/117441 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
PID2020-115270GB-I00 Not available Not available PDC2022-133429- I00 Not available Not available PID2021-125549OB-I00 Not available Not available MCIN AEI 10.13039 |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
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Springer |
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Springer |
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reponame:Docta Complutense instname:Universidad Complutense de Madrid (UCM) |
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Universidad Complutense de Madrid (UCM) |
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Docta Complutense |
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Docta Complutense |
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