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Genetic Programming Techniques for Glucose Prediction in People with Diabetes

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Genetic Programming Theory and Practice XX

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

Accurately predicting blood glucose levels in individuals with diabetes is essential for effectively managing and preventing complications. This paper explores the application of Grammatical Evolution, a genetic programming technique, for glucose prediction. It discusses how Grammatical Evolution has been employed in addressing various challenges related to glucose prediction, such as limited actual recorded data, prediction safety, interpretability of models, consideration of latent variables, and prognosis of hypoglycemia episodes. Building upon this research, the paper presents a comprehensive framework for glucose control that utilizes evolutionary techniques, primarily emphasizing structured grammatical evolution. The framework encompasses several stages, including data gathering, data augmentation, extraction of latent variability features, scenario clustering, structured grammatical evolution training, development of interpretable personal models, derivation of classification rules, glucose prediction, hypoglycemia alert, and glucose control. By harnessing the power of evolutionary algorithms, the framework optimizes model performance and adapts to individual patient characteristics. The proposed framework presents a promising approach to improve glucose monitoring and control, thereby contributing to better diabetes management and improved quality of life for patients.

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Acknowledgements

This work is supported by Spanish Government MINECO grants PID2021-125549OB-I00 and PDC2022-133429-I00.

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Authors and Affiliations

  1. Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  2. Universidad Complutense de Madrid, Madrid, Spain

    Jose Manuel Velasco, Daniel Parra & Oscar Garnica

Authors
  1. J. Ignacio Hidalgo
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  2. Jose Manuel Velasco
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  3. Daniel Parra
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  4. Oscar Garnica
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Corresponding author

Correspondence to J. Ignacio Hidalgo .

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Editors and Affiliations

  1. School of Information, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  2. Engineering Sciences Graduate Program, Tecnológico Nacional de México, IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Charles Ofria

  4. School of Computing, Queen's University, Kingston, ON, Canada

    Ting Hu

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Hidalgo, J.I., Velasco, J.M., Parra, D., Garnica, O. (2024). Genetic Programming Techniques for Glucose Prediction in People with Diabetes. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_6

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  • DOI: https://doi.org/10.1007/978-981-99-8413-8_6

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