Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming

J. Ignacio Hidalgo; Elisabeth Maria Mayrhuber; Stephan Winkler; Daniel Parra; J. Manuel Velasco; Oscar Garnica; José J. Zamorano-León

doi:10.1007/978-3-031-82957-4_31

Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming

J. Ignacio Hidalgo^*
, Elisabeth Maria Mayrhuber
, Stephan Winkler
, Daniel Parra
, J. Manuel Velasco
, Oscar Garnica
, José J. Zamorano-León

^*Corresponding author for this work

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

Obesity, a condition influenced by genetic, environmental, and behavioral factors, poses significant health risks. This study leverages the GenObIA dataset, collected from January 2018 to June 2022, which includes extensive sociodemographic, environmental, behavioral, and genetic data, to model the risk of overweight and obesity using genetic programming techniques. Our aim is to develop predictive models to identify individuals at risk of these conditions, thereby facilitating early intervention and prevention strategies. Through structured grammatical evolution and symbolic regression by genetic programming, we generate interpretable machine learning models that reveal the influence of genetic variants, particularly single nucleotide polymorphisms, on obesity. Our findings underscore the importance of age, metabolic syndrome, and lifestyle factors such as alcohol consumption in predicting obesity. The models indicate potential, but further refinement in data preprocessing and model training is necessary to improve prediction reliability. This research contributes to a deeper understanding of obesity and supports the development of targeted public health interventions.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers
Editors	Alexis Quesada-Arencibia, Michael Affenzeller, Roberto Moreno-Díaz
Publisher	Springer
Pages	367-379
Number of pages	13
Volume	15
Edition	1
ISBN (Electronic)	978-3-031-82957-4
ISBN (Print)	978-3-031-82959-8
DOIs	https://doi.org/10.1007/978-3-031-82957-4_31
Publication status	Published - 2024
Event	EUROCAST 2024: 19th International Conference on Computer Aided Systems Theory - Museo Elder de la Ciencia y la Tecnología, Las Palmas de Gran Canaria, Spain Duration: 25 Feb 2024 → 1 Mar 2024 https://eurocast2024.fulp.ulpgc.es

Publication series

Name	Lecture Notes in Computer Science
Volume	15173 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	EUROCAST 2024
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	25.02.2024 → 01.03.2024
Internet address	https://eurocast2024.fulp.ulpgc.es

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-82957-4_31

Cite this

Hidalgo, J. I., Mayrhuber, E. M., Winkler, S., Parra, D., Velasco, J. M., Garnica, O., & Zamorano-León, J. J. (2024). Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming. In A. Quesada-Arencibia, M. Affenzeller, & R. Moreno-Díaz (Eds.), Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers (1 ed., Vol. 15, pp. 367-379). (Lecture Notes in Computer Science; Vol. 15173 LNCS). Springer. https://doi.org/10.1007/978-3-031-82957-4_31

Hidalgo, J. Ignacio ; Mayrhuber, Elisabeth Maria ; Winkler, Stephan et al. / Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming. Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. editor / Alexis Quesada-Arencibia ; Michael Affenzeller ; Roberto Moreno-Díaz. Vol. 15 1. ed. Springer, 2024. pp. 367-379 (Lecture Notes in Computer Science).

@inproceedings{65b961943f0f4bc0bf72617ae81d82e9,

title = "Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming",

abstract = "Obesity, a condition influenced by genetic, environmental, and behavioral factors, poses significant health risks. This study leverages the GenObIA dataset, collected from January 2018 to June 2022, which includes extensive sociodemographic, environmental, behavioral, and genetic data, to model the risk of overweight and obesity using genetic programming techniques. Our aim is to develop predictive models to identify individuals at risk of these conditions, thereby facilitating early intervention and prevention strategies. Through structured grammatical evolution and symbolic regression by genetic programming, we generate interpretable machine learning models that reveal the influence of genetic variants, particularly single nucleotide polymorphisms, on obesity. Our findings underscore the importance of age, metabolic syndrome, and lifestyle factors such as alcohol consumption in predicting obesity. The models indicate potential, but further refinement in data preprocessing and model training is necessary to improve prediction reliability. This research contributes to a deeper understanding of obesity and supports the development of targeted public health interventions.",

author = "Hidalgo, \{J. Ignacio\} and Mayrhuber, \{Elisabeth Maria\} and Stephan Winkler and Daniel Parra and Velasco, \{J. Manuel\} and Oscar Garnica and Zamorano-Le{\'o}n, \{Jos{\'e} J.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; EUROCAST 2024 : 19th International Conference on Computer Aided Systems Theory ; Conference date: 25-02-2024 Through 01-03-2024",

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booktitle = "Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers",

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Hidalgo, JI, Mayrhuber, EM , Winkler, S, Parra, D, Velasco, JM, Garnica, O & Zamorano-León, JJ 2024, Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming. in A Quesada-Arencibia, M Affenzeller & R Moreno-Díaz (eds), Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. 1 edn, vol. 15, Lecture Notes in Computer Science, vol. 15173 LNCS, Springer, pp. 367-379, EUROCAST 2024, Las Palmas de Gran Canaria, Spain, 25.02.2024. https://doi.org/10.1007/978-3-031-82957-4_31

Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming. / Hidalgo, J. Ignacio; Mayrhuber, Elisabeth Maria ; Winkler, Stephan et al.
Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. ed. / Alexis Quesada-Arencibia; Michael Affenzeller; Roberto Moreno-Díaz. Vol. 15 1. ed. Springer, 2024. p. 367-379 (Lecture Notes in Computer Science; Vol. 15173 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming

AU - Hidalgo, J. Ignacio

AU - Mayrhuber, Elisabeth Maria

AU - Winkler, Stephan

AU - Parra, Daniel

AU - Velasco, J. Manuel

AU - Garnica, Oscar

AU - Zamorano-León, José J.

PY - 2024

Y1 - 2024

N2 - Obesity, a condition influenced by genetic, environmental, and behavioral factors, poses significant health risks. This study leverages the GenObIA dataset, collected from January 2018 to June 2022, which includes extensive sociodemographic, environmental, behavioral, and genetic data, to model the risk of overweight and obesity using genetic programming techniques. Our aim is to develop predictive models to identify individuals at risk of these conditions, thereby facilitating early intervention and prevention strategies. Through structured grammatical evolution and symbolic regression by genetic programming, we generate interpretable machine learning models that reveal the influence of genetic variants, particularly single nucleotide polymorphisms, on obesity. Our findings underscore the importance of age, metabolic syndrome, and lifestyle factors such as alcohol consumption in predicting obesity. The models indicate potential, but further refinement in data preprocessing and model training is necessary to improve prediction reliability. This research contributes to a deeper understanding of obesity and supports the development of targeted public health interventions.

AB - Obesity, a condition influenced by genetic, environmental, and behavioral factors, poses significant health risks. This study leverages the GenObIA dataset, collected from January 2018 to June 2022, which includes extensive sociodemographic, environmental, behavioral, and genetic data, to model the risk of overweight and obesity using genetic programming techniques. Our aim is to develop predictive models to identify individuals at risk of these conditions, thereby facilitating early intervention and prevention strategies. Through structured grammatical evolution and symbolic regression by genetic programming, we generate interpretable machine learning models that reveal the influence of genetic variants, particularly single nucleotide polymorphisms, on obesity. Our findings underscore the importance of age, metabolic syndrome, and lifestyle factors such as alcohol consumption in predicting obesity. The models indicate potential, but further refinement in data preprocessing and model training is necessary to improve prediction reliability. This research contributes to a deeper understanding of obesity and supports the development of targeted public health interventions.

UR - https://www.scopus.com/pages/publications/105004253036

U2 - 10.1007/978-3-031-82957-4_31

DO - 10.1007/978-3-031-82957-4_31

M3 - Conference contribution

SN - 978-3-031-82959-8

VL - 15

T3 - Lecture Notes in Computer Science

SP - 367

EP - 379

BT - Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers

A2 - Quesada-Arencibia, Alexis

A2 - Affenzeller, Michael

A2 - Moreno-Díaz, Roberto

PB - Springer

T2 - EUROCAST 2024

Y2 - 25 February 2024 through 1 March 2024

ER -

Hidalgo JI, Mayrhuber EM , Winkler S, Parra D, Velasco JM, Garnica O et al. Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming. In Quesada-Arencibia A, Affenzeller M, Moreno-Díaz R, editors, Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers. 1 ed. Vol. 15. Springer. 2024. p. 367-379. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-82957-4_31

Modelling the Risk of Overweight and Obesity Based on the GenObiA Dataset Using Genetic Programming

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