Dynamic higher level learning Radial Basis Function for healthcare application

Neural Network making use of Radial Basis Function (RBF) in the hidden layer maps the input of a lower dimension to a higher dimensional space in order to make the input linearly separable. The traditional RBF model is normally referred as cognitive component. The major issues in the traditional model are large number of fixed neurons, use of complete training set, prior center selection etc,. These issues increase the computation time and architecture complexity. To overcome these issues, this paper proposes a novel Dynamic Higher Level Learning RBF (DHLRBF) architecture suitable for dynamic environment. The learning process of the cognitive component is controlled by the Higher Level Learning component such as Neuron addition and Sample deletion. The proposed work is applied for Health parameters to classify normal and abnormal category. The proposed DHLRBF is implemented and the results show that the model is efficient in terms of detection accuracy and time.