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Towards neural network model for insulin/glucose in diabetics-II
Published in
Volume: 29
Issue: 2
Pages: 227 - 232
In this work we extending our investigations for a general neural network model that resembles the interactions between glucose concentration levels and amount of insulin injected in the bodies of diabetics. We use real data for 70 different patients of diabetics and build on it our model. Two types of neural networks (NN's) are experimented in building that model; the first type is called the Levenberg-Marquardt (LM) training algorithm of multilayer feed forward neural network (NN), the other one is based on Polynomial Network (PN's). We do comparisons between the two models based on their performance. The design stages mainly consist of training, testing, and validation. A linear regression between the output of the multi-layer feed forward neural network trained by LM algorithm (abbreviated by LM NN) and the actual outputs shows that the LM NN is a better model. The PN's have proved to be good static "mappers", but their performance is degraded when used in modelling a dynamical system. The LM NN based model still proved that it can potentially be used to build a theoretical general regulator controller for insulin injections and, hence, can reflect an idea about the types and amounts of insulin required for patients.
About the journal
JournalInformatica (Ljubljana)
Open AccessNo