technical-note

Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation

Authors:

J. Ignacio Hidalgo,

J. Manuel Colmenar,

Jose L. Risco-Martín,

Esther Maqueda,

Jose Antonio Rubio,

Alfredo Cuesta-Infante,

Juan LancharesAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1305 - 1312

https://doi.org/10.1145/2598394.2609856

Published: 12 July 2014 Publication History

Abstract

Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, a lot of research has been made to improve the quality of life of the diabetic patient, especially in the automation of glucose level control. One of the main problems that arises in the (semi) automatic control of diabetes, is to obtain a model that explains the behavior of blood glucose levels with insulin, food intakes and other external factors, fitting the characteristics of each individual or patient. Recently, Grammatical Evolution (GE), has been proposed to solve this lack of models. A proposal based on GE was able to obtain customized models of five in-silico patient data with a mean percentage average error of 13.69%, modeling well also both hyper and hypoglycemic situations. In this paper we have extended the study of Error Grid Analysis (EGA) to prediction models in up to 8 in-silico patients. EGA is commonly used in Endocrinology to test the clinical significance of differences between measurements and real value of blood glucose, but has not been used before as a metric in obtention of glycemia models.

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Cited By

Rajković KDabić-Stanković KStanković JAćimović MĐukanović NNikolin B(2020)Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithmComputers in Biology and Medicine10.1016/j.compbiomed.2020.104045126:COnline publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1016/j.compbiomed.2020.104045
Contreras IOviedo SVettoretti MVisentin RVehí J(2017)Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological modelsPLOS ONE10.1371/journal.pone.018775412:11(e0187754)Online publication date: 7-Nov-2017
https://doi.org/10.1371/journal.pone.0187754

Index Terms

Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation

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cover image ACM Conferences

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

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Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Rajković KDabić-Stanković KStanković JAćimović MĐukanović NNikolin B(2020)Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithmComputers in Biology and Medicine10.1016/j.compbiomed.2020.104045126:COnline publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1016/j.compbiomed.2020.104045
Contreras IOviedo SVettoretti MVisentin RVehí J(2017)Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological modelsPLOS ONE10.1371/journal.pone.018775412:11(e0187754)Online publication date: 7-Nov-2017
https://doi.org/10.1371/journal.pone.0187754

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