Urinary nucleosides as potential tumor markers evaluated by learning vector quantization
Artificial Intelligence in Medicine
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The advancement in computer technology has reinforced the development of Artificial Neural Networks (ANN), so that they are used in a wide area of application fields. Medicine is one of these fields. ANNs are suitable for disease prognosis since there is no need to provide diagnosis rules to identify the disease, but a set of examples that represents the variations of disease. This study explores the use of various ANN architectures in vesicoureteral reflux (VUR) prognosis. It is resulted that the performance of an ANN with a hidden layer with hyperbolic tangent sigmoid transfer function and an output layer with saturating linear transfer function is remarkably better against other more complicated structures. The proposed ANN prognoses 100% of the pathological cases (true positive). The aim of proposed network is not to replace the specialists, but to assist general physicians and specialists in predicting VUR in order to avoid the unnecessary exposure of children in voiding cystourethrogram.