The Neural Network Selection for a Medical Diagnostic System using an Artificial Data Set

Abstract

The paper describes experiments with a neural network selection that works as a conclusion-making unit of walk-abnormalities diagnosis. The diagnostic interfaces described in this paper provide the user with various tools for the disease analysis. They are having a pressure and load distribution on the foot, while taking into account the individual characteristics of the patient standing and walking (1), (2), (3). Various visualisation options give the user many aims in putting the diagnosis anyhow, in order to simplify the diagnostic process several methods for the data record filtering have been implemented. The discussed methods of the neural network selection and training show how to avoid difficulties with limited number of available data records, needed for the conclusion algorithms effectiveness improvement.