Hybrid clustering-classification neural network in the medical diagnostics of reactive arthritis

Self-organizing maps (SOM) and neural networks of learning vector quantization (LVQ) have seen extensive use for solving different problems in Data Mining domain (clustering, classification, fault detection and compression of information etc.). This type of neural networks was proposed by T. Kohonen [1, 2] and represents, in fact, a single-layer feedforward architecture, which provides an operator for mapping of input space into the output space. Operation-wise SOM and LVQ are quite similar to each neuron is fed input signal (sample) producing output, which is used during competition stage to determine winning neuron - usually the one with maximum output signal value. Vector of synaptic weights for winning neuron is the one closest to the input sample in terms of the metric chosen (which is Euclidian metric in most cases). Next is neurons adjustment phase.