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Link to original content: https://doi.org/10.1007/s10916-017-0788-2
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Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods

  • Patient Facing Systems
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Abstract

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

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Notes

  1. Also see Howard and Bowles: “The Two Most Important Algorithms in Predictive Modeling Today”, STRATA Conference, 2012, O’Reilly.

  2. http://www.alglib.net/

  3. http://dev.heuristiclab.com/

  4. The expression in Fig. 3 includes also some constants (< c t e > ) not present in Eq. 16 for the sake of simplicity of the explanation.

  5. We use wrapping of 5 because with lower values we obtain high percentages of non valid solutions during the initial generations.

  6. Execution times using an Intel 7 processor with 8GB RAM running Windows 10 as operating system at 3.6 GHz.

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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).

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Authors and Affiliations

  1. Adaptive and Bioinspired System Group, School of Informatics, Universidad Complutense de Madrid, C/ Profesor José García Santesmases 9, 28040, Madrid, Spain

    J. Ignacio Hidalgo, Oscar Garnica & Juan Lanchares

  2. Universidad Rey Juan Carlos, C/ Tulipán s/n, 28933, Móstoles, Spain

    J. Manuel Colmenar

  3. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Softwarepark 11, 4232, 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. Gabriel Kronberger
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  4. Stephan M. Winkler
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  5. Oscar Garnica
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  6. Juan Lanchares
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Correspondence to J. Ignacio Hidalgo.

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Hidalgo, J., Colmenar, J., Kronberger, G. et al. Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods. J Med Syst 41, 142 (2017). https://doi.org/10.1007/s10916-017-0788-2

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