RT Book, Section
T1 Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution
A1 Hidalgo Pérez, José Ignacio
A1 Colmenar Verduosé Manuel, 
A1 Velasco Cabo, José Manuel
A1 Kronberger, Gabriel
A1 Winkler, Stephan M.
A1 Garnica Alcázar, Antonio Óscar
A1 Lanchares Dávila, Juan
A2 Ryan, C.
A2 O'Neill, M.
A2 Collins, J.
AB One the most relevant application areas of artificial intelligence andmachine 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 basedon 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.
PB Springer Nature
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/117417
UL https://hdl.handle.net/20.500.14352/117417
LA eng
NO J. I. Hidalgo et al., «Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution», en Handbook of Grammatical Evolution, C. Ryan, M. O’Neill, y J. Collins, Eds., Cham: Springer International Publishing, 2018, pp. 367-393. doi: 10.1007/978-3-319-78717-6_15.
NO Ministerio de Ciencia, Innovación y Universidades(España)
NO Ministerio de Educación, Formación Profesional y Deportes (España)
NO Austrian Research Promotion Agency
DS Docta Complutense
RD 10 abr 2025