RT Journal Article
T1 Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging
A1 Hidalgo Pérez, José Ignacio
A1 Botella Serrano, Marta
A1 Velasco Cabo, José Manuel
A1 Garnica Alcázar, Antonio Óscar
A1 Cervigón Ruckauer, Carlos
A1 Martínez, Remedios
A1 Aramendi, Aranzazu
A1 Maqueda, Esther
A1 Lanchares Dávila, Juan
AB Diabetes Mellitus is a disease affecting more and more people every year. Depending on the kind of diabetes and sometimes on the stage of the illness, diabetic patients have to inject some amount of artificial insulin, namely bolus, before the meals, to make up the absence or malfunctioning of their natural insulin. This decision is a difficult task since they need to estimate the number of carbohydrates they are going to ingest, take into account the past and future circumstances, know the past values of glucose, evaluate if the effect of previously injected insulin has already finished and any other relevant information. In this paper, we present and compare a set of methodologies to automate the decision of the insulin bolus, which reduces the number of dangerous predictions. We combine two different data enrichment techniques based on Markov chains with grammatical evolution engines to generate models of blood glucose, and univariate marginal distribution algorithms and bagging techniques to select the set of models to assemble. In particular, we propose the Random-GE procedure, an adaptation of Random Forests to Grammatical Evolution, which leads to excellent prediction models, with a simple configuration and a reduced execution time. The ensemble gives the prediction of glucose for a duple of food and insulins, helping patients in the selection of the appropriate bolus to maintain healthy glucose levels after the meals. Experimental results show that our models get more accurate and robust predictions than previous approaches.
PB Elsevier
YR 2020
FD 2020-03
LK https://hdl.handle.net/20.500.14352/117425
UL https://hdl.handle.net/20.500.14352/117425
LA eng
NO J. I. Hidalgo et al., «Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging», Applied Soft Computing, vol. 88, p. 105923, mar. 2020, doi: 10.1016/j.asoc.2019.105923.
NO Fundación Eugenio Rodríguez Pascual
NO Ministerio de Economía, Comercio y Empresa (España)
NO Comunidad de Madrid
DS Docta Complutense
RD 9 abr 2025