Predicting glycemia in diabetic patients by evolutionary computation and continuous glucose monitoring

J. Manuel Colmenar, Stephan M. Winkler, Gabriel Kronberger, Esther Maqueda, Marta Botella, J. Ignacio Hidalgo

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

7 Citations (Scopus)

Abstract

Diabetes mellitus is a disease that affects more than three hundreds million people worldwide. Maintaining a good control of the disease is critical to avoid not only severe long-term complications but also dangerous short-term situations. Diabetics need to decide the appropriate insulin injection, thus they need to be able to estimate the level of glucose they are going to have after a meal. In this paper we use machine learning techniques for predicting glycemia in diabetic patients. The algorithms utilize data collected from real patients by a continuous glucose monitoring system, the estimated number of carbohydrates, and insulin administration for each meal. We compare (1) non-linear regression with fixed model structure, (2) identification of prognosis models by symbolic regression using genetic programming, (3) prognosis by k-nearest-neighbor time series search, and (4) identification of prediction models by grammatical evolution. We consider predictions horizons of 30, 60, 90 and 120 minutes.

Original languageEnglish
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherAssociation for Computing Machinery, Inc
Pages1393-1400
Number of pages8
ISBN (Electronic)9781450343237
DOIs
Publication statusPublished - 20 Jul 2016
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States
Duration: 20 Jul 201624 Jul 2016

Publication series

NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Conference

Conference2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
CountryUnited States
CityDenver
Period20.07.201624.07.2016

Keywords

  • Diabetes
  • Genetic programming
  • Grammatical evolution
  • Symbolic regression

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