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Data-Based Identification of Prediction Models for Glucose

Published: 11 July 2015 Publication History

Abstract

Diabetes mellitus is a disease that affects to hundreds of million of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. One of the main problems that arise in the (semi) automatic control of diabetes, is to get a model explaining how glucose levels in blood vary with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. In this paper we compare genetic programming techniques with a set of classical identification techniques: classical simple exponential smoothing, Holt's smoothing (linear, exponential and damped), classical Holt and Winters methods and auto regressive integrated moving average modeling. We consider predictions horizons of 30, 60, 90 and 120 minutes. Experimental results shows the difficulty of predicting glucose values for more than 60 minutes and the necessity of adapt GP techniques for those dynamic environments.

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Cited By

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  • (2018)Identification of Models for Glucose Blood Values in Diabetics by Grammatical EvolutionHandbook of Grammatical Evolution10.1007/978-3-319-78717-6_15(367-393)Online publication date: 12-Sep-2018
  • (2017)Data Based Prediction of Blood Glucose Concentrations Using Evolutionary MethodsJournal of Medical Systems10.1007/s10916-017-0788-241:9(1-20)Online publication date: 1-Sep-2017

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cover image ACM Conferences
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
July 2015
1568 pages
ISBN:9781450334884
DOI:10.1145/2739482
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 July 2015

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Author Tags

  1. diabetes
  2. genetic programming
  3. modeling

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  • Research-article

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  • Austrian Funding Agency FFG
  • Spanish Minister of Science and Innovation

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GECCO '15
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View all
  • (2018)Identification of Models for Glucose Blood Values in Diabetics by Grammatical EvolutionHandbook of Grammatical Evolution10.1007/978-3-319-78717-6_15(367-393)Online publication date: 12-Sep-2018
  • (2017)Data Based Prediction of Blood Glucose Concentrations Using Evolutionary MethodsJournal of Medical Systems10.1007/s10916-017-0788-241:9(1-20)Online publication date: 1-Sep-2017

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