Publication
This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity point of view. In our study, a model of a multilayer perceptron with a training algorithm based on an error back-propagation method with a momentum component was applied. Data analysis was performed using the perceptron with one hidden layer and training methods with incremental and cumulative neuron weight updating. We have examined an influence of the order in the training data file on the final prediction results. The efficiency of the proposed methodology in the bladder urothelial cancer prediction after cystectomy is on the level of 90%, which is the best result ever reported. Best outcomes one achieves for 5 neurons in the hidden layer.
The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...