Modelling medical diagnosis through kohonen self-organizable map

Abstract

The objective of this work is the consideration and implementation of some basic premises used in the learning process in Artificial Neural Networks (ANN`s). Initially the net will be trained starting from the Neusciences simulator: ActiveX to, starting from the result of this simulation, be compared with the algorithm of competitive learning through the Kohonen Self-Organizable Map. The chosen domain for the implementation of the learning algorithms was the application in the Clinical Diagnosis of the Convulsive Crises, based on the International Classification League Against Epilepsy ILAI/81. According to the results of the simulator and using the Learning Vector Quantization (LVQ1) technique with the 2x2 configuration, the base of training of the network showed a performance of 69,76 and 71,31% respectively. For the test set of the simulator and the LVQ1 technique the network obtained an index satisfactory of classification of 80% and 100% respectively. With the 5x5 configuration to increase the index of classification.