Towards Neural Network Model for Insulin/Glucose in Diabetics


Raed Abu Zitar

  College of Information Technology

Phil adel phia University




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 (NNís) 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.