Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution

J. Ignacio Hidalgo, J. Manuel Colmenar, J. Manuel Velasco, Gabriel Kronberger, Stephan Winkler, Oscar Garnica, Juan Lanchares

Research output: Chapter in Book/Report/Conference proceedingsChapter

1 Citation (Scopus)

Abstract

One the most relevant application areas of artificial intelligence and machine 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 based on 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.

Original languageEnglish
Title of host publicationHandbook of Grammatical Evolution
PublisherSpringer International Publishing
Pages367-393
Number of pages27
ISBN (Electronic)9783319787176
ISBN (Print)978-3-319-78716-9
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint Dive into the research topics of 'Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution'. Together they form a unique fingerprint.

Cite this