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

  • J. Ignacio HidalgoEmail author
  • J. Manuel Colmenar
  • J. Manuel Velasco
  • Gabriel Kronberger
  • Stephan M. Winkler
  • Oscar Garnica
  • Juan Lanchares


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.


Grammatical Evolution (GE) Data Augmentation (DA) Univariate Marginal Distribution Algorithm (UMDA) UMDA Algorithm Continuous Glucose Monitoring System (CGMS) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. 1.
    G. Sparacino, F. Zanderigo, S. Corazza, A. Maran, A. Facchinetti, C. Cobelli, Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans. Biomed. Eng. 54(5), 931–937 (2007)CrossRefGoogle Scholar
  2. 2.
    J.I. Hidalgo, E. Maqueda, J.L. Risco-Martín, A. Cuesta-Infante, J.M. Colmenar, J. Nobel, gIUCmodel: a monitoring and modeling system for chronic diseases applied to diabetes. J. Biomed. Inform. 48, 183–192 (2014)CrossRefGoogle Scholar
  3. 3.
    J.I. Hidalgo, J.M. Colmenar, G. Kronberger, S.M. Winkler, O. Garnica, J. Lanchares, Data based prediction of blood glucose concentrations using evolutionary methods. J. Med. Syst. 41(9), 142 (2017)Google Scholar
  4. 4.
    J.M. Velasco, O. Garnica, S. Contador, J.M. Colmenar, E. Maqueda, M. Botella, J. Lanchares, J.I. Hidalgo, Enhancing grammatical evolution through data augmentation: application to blood glucose forecasting, in European Conference on the Applications of Evolutionary Computation (Springer, Berlin, 2017), pp. 142–157Google Scholar
  5. 5.
    B. Hansen, I. Matytsina, Insulin administration: selecting the appropriate needle and individualizing the injection technique. Expert Opin. Drug Deliv. 8(10), 1395–1406 (2011)CrossRefGoogle Scholar
  6. 6.
    J. Weissberg-Benchell, J. Antisdel-Lomaglio, R. Seshadri, Insulin pump therapy. Diabetes Care 26(4), 1079–1087 (2003)CrossRefGoogle Scholar
  7. 7.
    P.A. Bakhtiani, L.M. Zhao, J. El Youssef, J.R. Castle, W.K. Ward, A review of artificial pancreas technologies with an emphasis on bi-hormonal therapy. Diabetes. Obes. Metab. 15(12), 1065–1070 (2013)CrossRefGoogle Scholar
  8. 8.
    J.I. Hidalgo, J.M. Colmenar, J.L. Risco-Martin, A. Cuesta-Infante, E. Maqueda, M. Botella, J.A. Rubio, Modeling glycemia in humans by means of grammatical evolution. Appl. Soft Comput. 20, 40–53 (2014)CrossRefGoogle Scholar
  9. 9.
    J.M. Velasco, S. Winkler, J.I. Hidalgo, O. Garnica, J. Lanchares, J.M. Colmenar, E. Maqueda, M. Botella, J.-A. Rubio, Data-based identification of prediction models for glucose, in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2015), pp. 1327–1334Google Scholar
  10. 10.
    J.M. Colmenar, S.M. Winkler, G. Kronberger, E. Maqueda, M. Botella, J.I. Hidalgo, Predicting glycemia in diabetic patients by evolutionary computation and continuous glucose monitoring, in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (ACM, New York, 2016), pp. 1393–1400Google Scholar
  11. 11.
    Adaptive and Bioinspired Systems Group, ABSys JECO (Java Evolutionary COmputation) library (2016),
  12. 12.
    J.M. Colmenar, J.I. Hidalgo, J. Lanchares, O. Garnica, J.-L. Risco, I. Contreras, A. Sánchez, J.M. Velasco, Compilable phenotypes: speeding-up the evaluation of glucose models in grammatical evolution, in European Conference on the Applications of Evolutionary Computation (Springer International Publishing, Berlin, 2016), pp. 118–133Google Scholar
  13. 13.
    M. O’Neill, C. Ryan, Grammatical evolution by grammatical evolution: the evolution of grammar and genetic code, in European Conference on Genetic Programming (Springer, Berlin, 2004), pp. 138–149Google Scholar
  14. 14.
    I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments, vol. 194 (Springer, Berlin, 2009)Google Scholar
  15. 15.
    D. Moreno-Salinas, E. Besada-Portas, J. López-Orozco, D. Chaos, J. de la Cruz, J. Aranda, Symbolic regression for marine vehicles identification. IFAC-PapersOnLine 48(16), 210–216 (2015)CrossRefGoogle Scholar
  16. 16.
    M. Kommenda, A. Beham, M. Affenzeller, G. Kronberger, Complexity measures for multi-objective symbolic regression, in International Conference on Computer Aided Systems Theory (Springer, Berlin, 2015), pp. 409–416Google Scholar
  17. 17.
    M.A. Tanner, W.H. Wong, From EM to data augmentation: the emergence of MCMC Bayesian computation in the 1980s. ArXiv e-prints, Apr. 2011Google Scholar
  18. 18.
    M. Yadav, P. Malhotra, L. Vig, K. Sriram, G. Shroff, ODE - augmented training improves anomaly detection in sensor data from machines. CoRR, abs/1605.01534 (2016)Google Scholar
  19. 19.
    A. Kumar, L. Cowen, Augmented training of hidden Markov models to recognize remote homologs via simulated evolution. Bioinformatics 25(13), 1602–1608 (2009)CrossRefGoogle Scholar
  20. 20.
    M. Pelikan, H. Mühlenbein, Marginal distributions in evolutionary algorithms, in Proceedings of the International Conference on Genetic Algorithms Mendel, vol. 98 (Citeseer, 1998) pp. 90–95Google Scholar
  21. 21.
    H. Mühlenbein, The equation for response to selection and its use for prediction. Evol. Comput. 5, 303–346 (1997)CrossRefGoogle Scholar
  22. 22.
    S.S. Shapiro, M.B. Wilk, An analysis of variance test for normality (complete samples). Biometrika 52(3), 591–611 (1965)MathSciNetCrossRefGoogle Scholar
  23. 23.
    C. Ryan, A rebuttal to Whigham, Dick, and Maclaurin by one of the inventors of grammatical evolution: commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin. Genet. Program Evolvable Mach. 18, 385–389 (2017)Google Scholar
  24. 24.
    P.A. Whigham, G. Dick, J. Maclaurin, On the mapping of genotype to phenotype in evolutionary algorithms. Genet. Program Evolvable Mach. 18, 353–361 (2017)CrossRefGoogle Scholar
  25. 25.
    S. Verel, G. Ochoa, M. Tomassini, Local optima networks of NK landscapes with neutrality. IEEE Trans. Evol. Comput. 15(6), 783–797 (2011)CrossRefGoogle Scholar
  26. 26.
    G. Ochoa, M. Tomassini, S. Vérel, C. Darabos, A study of NK landscapes’ basins and local optima networks, in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2008), pp. 555–562Google Scholar
  27. 27.
    W. Clarke, D. Cox, L. Gonder-Frederick, W. Carter, S. Pohl, Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10(5), 622–628 (1987)CrossRefGoogle Scholar
  28. 28.
    J. Parkes, S. Slatin, S. Pardo, B. Ginsberg, A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabetes Care 23(8), 1143–1148 (2000)CrossRefGoogle Scholar
  29. 29.
    J.M. Colmenar, J.I. Hidalgo, J. Lanchares, O. Garnica, J.-L. Risco, I. Contreras, A. Sánchez, J.M. Velasco, Compilable phenotypes: speeding-up the evaluation of glucose models in grammatical evolution, in European Conference on the Applications of Evolutionary Computation (Springer, Berlin, 2016), pp. 118–133Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • J. Ignacio Hidalgo
    • 1
    Email author
  • J. Manuel Colmenar
    • 2
  • J. Manuel Velasco
    • 1
  • Gabriel Kronberger
    • 3
  • Stephan M. Winkler
    • 3
  • Oscar Garnica
    • 1
  • Juan Lanchares
    • 1
  1. 1.Adaptive and Bioinspired System GroupUniversidad Complutense de MadridMadridSpain
  2. 2.Universidad Rey Juan CarlosMóstolesSpain
  3. 3.University of Applied Sciences Upper AustriaHeuristic and Evolutionary Algorithms LaboratoryHagenbergAustria

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