A Neuron Model With Synaptic Nonlinearities In A Dendritic Tree For Liver Disorders

With the number of deaths due to liver diseases increasing steadily in recent years, early detection and treatment of these diseases has been one of the most active research fields using computational intelligence techniques. In this paper, we propose a more realistic single-neuron model with synaptic nonlinearities in a dendritic tree for liver disorder diagnosis. The computation on the neuron is performed as a combination of dimensional reduction and nonlinearity, which has a neuron-pruning function that can remove useless synapses and dendrites during learning, forming a distinct synaptic and dendritic morphology. The nonlinear interactions in a dendrite tree are expressed using the Boolean logic AND (conjunction), OR (disjunction), and NOT (negation), which can be easily implemented in hardware. Furthermore, an error backpropagation (BP) algorithm is used to train the neuron model, and the performance is compared with a traditional BP neural network in terms of accuracy, sensitivity, and specificity. We use the BUPA liver disorder datasets obtained from the UCI Machine Learning Repository to verify the proposed method. Simulation results show promise for the use of this single-neuron model as an effective pattern classification method in liver disorder diagnostics. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.