RT Journal Article
T1 Glucose forecasting using genetic programming and latent glucose variability features
A1 Contador, Sergio
A1 Velasco Cabo, José Manuel
A1 Garnica Alcázar, Antonio Óscar
A1 Hidalgo Pérez, José Ignacio
AB This paper investigates a set of genetic programming methods to obtain accurate predictions of subcutaneous glucose values from diabetic patients. We explore the usefulness of different features that identify the latent glucose variability. New features, including average glucose, glucose variability and glycemic risk, are generated as input variables of the genetic programming algorithm in order to improve the accuracy of the models in the prediction phase. The performance of traditional genetic programming, and models created with classified glucose values, are compared to those using latent glucose variability features. We experimented with a set of 18 different features and we also performed a study of the importance of the variables in the models. The Bayesian statistical analysis shows that the use of glucose variability as latent variables improved the predictions of the models, not only in terms of RMSE, but also in the reduction of dangerous predictions, i.e., those predictions that could lead to wrong decisions in the clinical practice.
PB Elsevier
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/117371
UL https://hdl.handle.net/20.500.14352/117371
LA eng
DS Docta Complutense
RD 19 dic 2025