1 INTRODUCTION The main objective of this article is to explore the neural network technique for predicting certain features indicative of non-organ-confined prostate cancer upon the basis of the flows of certain diagnostic tests administered to patients suffering from prostate cancer.

1 INTRODUCTION

The main objective of this article is to explore the neural network technique for predicting certain features indicative of non-organ-confined prostate cancer upon the basis of the flows of certain diagnostic tests administered to patients suffering from prostate cancer. Our approach is completely Bayesian and provides the posterior (or predictive) probability of neighborhood of these features in the patients based upon certain inputs. Physicians can then make decisions forward the basis of these probabilities, particularly, in marginal cases (eg when these posterior probabilities are in the neighborhood of 50%) to order further diagnostic proofs rather than making an