Optimization of hidden layer in a neural network used to predict bladder-cancer patient-survival

A problem of establishing an optimal number of neurons in a hidden layer of a perceptron network used to predict survival time of patients with bladder cancer has been discussed. Our considerations are important in postoperative treatments of this illness. The applied neural network is a three layer one with one hidden layer. Its designing and testing investigations were performed in MATLAB environment. As the network teaching method, classical error back-propagation algorithm with a momentum factor was applied. For the assumed model of the problem, we have obtained a characteristic graph of the function describing false results of the survival predictions. We have utilized a representative training set and investigated the network for different number of neurons in the hidden layer. A distinct error minimum has been observed for 13 neurons in this layer. It is not out of the question that the character of the achieved curve is repeatable for different input/output vectors and may be practicable for determining the number of neurons in networks dedicated to biological models.