Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods

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@Article{Hidalgo2017b,

• author = "J. Ignacio Hidalgo and J. Manuel Colmenar and Gabriel Kronberger and Stephan M. Winkler and Oscar Garnica and Juan Lanchares",
• title = "Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods",
• journal = "Journal of Medical Systems",
• year = "2017",
• volume = "41",
• number = "9",
• pages = "142",
• month = sep,
• note = "Special issue on Patient Facing Systems",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, Diabetes, Glucose prediction, Continuous glucose monitoring, Evolutionary computation",
• ISSN = "1573-689X",
• DOI = "doi:10.1007/s10916-017-0788-2",
• size = "20 pages",
• 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 modelling 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 neighbours, 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 analyse our experimental results using the Clarke error grid metric and see that 90percent of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10percent of serious errors (category D) and approximately 0.5percent 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.",

}

Genetic Programming entries for Jose Ignacio Hidalgo Perez J Manuel Colmenar Gabriel Kronberger Stephan M Winkler Oscar Garnica J Lanchares

Citations