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Handbook of Grammatical Evolution pp 367–393Cite as

Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution

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Abstract

One the most relevant application areas of artificial intelligence and machine learning in general is medical research. We here focus on research dedicated to diabetes, a disease that affects a high percentage of the population worldwide and that is an increasing threat due to the advance of the sedentary life in the big cities. Most recent studies estimate that it affects about more than 410 million people in the world. In this chapter we discuss a set of techniques based on GE to obtain mathematical models of the evolution of blood glucose along the time. These models help diabetic patients to improve the control of blood sugar levels and thus, improve their quality of life. We summarize some recent works on data preprocessing and design of grammars that have proven to be valuable in the identification of prediction models for type 1 diabetics. Furthermore, we explain the data augmentation method which is used to sample new data sets.

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Notes

  1. 1.

    There are other types of diabetes with lower incidence such as problems caused by genetic defects affecting insulin action, induced by drugs, or other syndromes.

  2. 2.

    T1DM stands for Type 1 Diabetes Mellitus, from Latin mel (“honey”).

  3. 3.

    On June 6, 2012, the Clinical Research Ethics Committee of the Hospital of Alcalá de Henares (Spain) authorized the use of the data collected, provided that the privacy of the data is ensured and the informed consent of patients is made.

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Acknowledgements

This work was partially supported by the Spanish Government Minister of Science and Innovation under grants TIN2014-54806-R and TIN2015-65460-C2. J. I. Hidalgo also acknowledges the support of the Spanish Ministry of Education mobility grant PRX16/00216. S. M. Winkler and G. Kronberger acknowledge the support of the Austrian Research Promotion Agency (FFG) under grant #843532 (COMET Project Heuristic Optimization in Production and Logistics). The authors would like to thank the help of the medical staff: Marta Botella, Esther Maqueda, Aranzazu Aramendi-Zurimendi and Remedios Martínez-Rodríguez.

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Authors and Affiliations

  1. Adaptive and Bioinspired System Group, Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo, J. Manuel Velasco, Oscar Garnica & Juan Lanchares

  2. Universidad Rey Juan Carlos, Móstoles, Spain

    J. Manuel Colmenar

  3. University of Applied Sciences Upper Austria, Heuristic and Evolutionary Algorithms Laboratory, Hagenberg, Austria

    Gabriel Kronberger & Stephan M. Winkler

Authors
  1. J. Ignacio Hidalgo
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  2. J. Manuel Colmenar
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  3. J. Manuel Velasco
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  4. Gabriel Kronberger
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  5. Stephan M. Winkler
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  6. Oscar Garnica
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  7. Juan Lanchares
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Correspondence to J. Ignacio Hidalgo .

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Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    Conor Ryan

  2. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  3. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    JJ Collins

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Hidalgo, J.I. et al. (2018). Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-78717-6_15

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