Towards Neural Network Model for Insulin/Glucose in
Diabetics

**Raed Abu Zitar**

e-mail: rzitar@philadelphia.edu.jo

**Abstract: **In
this work we look 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 Radial Basis Function (RBF) neural network. 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.
This model can be potentially 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.

**Keywords**:
Levenberg-Marquardt Neural Network, Radial Basis Function Neural Network,
Diabetics, Insulin.