Statistical and Soft Feature Evaluation Indices for Prostate Cancer Prognostic Factor Assessment

In this paper, statistical, artificial neural networks and fuzzy based feature evaluation indices are analysed in order to determine the importance of prostate cancer prognostic markers. Seven prognostic markers are assessed in terms of 3 output classes using logistic regression as a statistical method, multilayer feedforward back propagation neural networks (MLFFBPNN) as a neural network tool, and fuzzy k-nearest neighbour algorithm (FK-NN) as a fuzzy method. The efficiency of the MLFFBPNN and FK-NN based indices is shown where the statistically based one fails to identify a clinically significant factor