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GLYCAEMIA REGULATION PREDICTIVE CONTROL SYSTEMS PERFORMANCES EVALUATION - A Comparative Study of Neural and Mathematical Models

Topics: Decision Support Systems; Development of Assistive Technology; Knowledge Management; Pattern Recognition and Machine Learning; Software Systems in Medicine; Therapeutic Systems and Technologies

Authors: Nathanaël Cottin ; Olivier Grunder and Abdellah ElMoudni

Affiliation: Université de Technologie de Belfort Montbéliard, France

Keyword(s): Diabetes, Glycaemia regulation, Insulin, NPC, Artificial neural network, Resilient propagation.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Data Engineering ; Decision Support Systems ; Development of Assistive Technology ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Ontologies and the Semantic Web ; Pattern Recognition and Machine Learning ; Society, e-Business and e-Government ; Software Systems in Medicine ; Therapeutic Systems and Technologies ; Web Information Systems and Technologies

Abstract: Type 1 blood glucose regulation remains a complex problem to simulate. Different blood glucose control schemes for insulin-dependent diabetes therapies and systems have been proposed in the literature. This article presents an adaptative predictive control system for glycaemia regulation based on feedforward Artificial Neural Networks trained with the resilient propagation (RPROP) method. Experiments performed on a mathematical (theoretical) compensation model and our system aim to objectively compare the behaviour of each approach when both exact and perturbated data are presented. These experiments, which make use of a virtual patient, not only cover the ANN’s best configuration and training parameters on exact training information, they also demonstrate the accuracy of the neural approach when up to 20% perturbated data are supplied. As a result of the experiments on perturbated data, the neural approach gives slightly better evaluations than the theoretical model. This demonstrat es the neural system’s ability to adapt to perturbated environments. (More)

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Paper citation in several formats:
Cottin, N.; Grunder, O. and ElMoudni, A. (2011). GLYCAEMIA REGULATION PREDICTIVE CONTROL SYSTEMS PERFORMANCES EVALUATION - A Comparative Study of Neural and Mathematical Models. In Proceedings of the International Conference on Health Informatics (BIOSTEC 2011) - HEALTHINF; ISBN 978-989-8425-34-8; ISSN 2184-4305, SciTePress, pages 525-528. DOI: 10.5220/0003138805250528

@conference{healthinf11,
author={Nathanaël Cottin. and Olivier Grunder. and Abdellah ElMoudni.},
title={GLYCAEMIA REGULATION PREDICTIVE CONTROL SYSTEMS PERFORMANCES EVALUATION - A Comparative Study of Neural and Mathematical Models},
booktitle={Proceedings of the International Conference on Health Informatics (BIOSTEC 2011) - HEALTHINF},
year={2011},
pages={525-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003138805250528},
isbn={978-989-8425-34-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics (BIOSTEC 2011) - HEALTHINF
TI - GLYCAEMIA REGULATION PREDICTIVE CONTROL SYSTEMS PERFORMANCES EVALUATION - A Comparative Study of Neural and Mathematical Models
SN - 978-989-8425-34-8
IS - 2184-4305
AU - Cottin, N.
AU - Grunder, O.
AU - ElMoudni, A.
PY - 2011
SP - 525
EP - 528
DO - 10.5220/0003138805250528
PB - SciTePress