Review: Application of artificial neural networks in the diagnosis of urological dysfunctions

Authors:
David Gil;Magnus Johnsson;Juan Manuel Garcia Chamizo;Antonio Soriano Paya;Daniel Ruiz Fernandez
Affiliations:
Computing Technology and Data Processing, University of Alicante, Alicante, Spain;Lund University Cognitive Science, Department of Computer Science, Lund University, Lund, Sweden;Computing Technology and Data Processing, University of Alicante, Alicante, Spain;Computing Technology and Data Processing, University of Alicante, Alicante, Spain;Computing Technology and Data Processing, University of Alicante, Alicante, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

In this article, we evaluate the work out of some artificial neural network models as tools for support in the medical diagnosis of urological dysfunctions. We develop two types of unsupervised and one supervised neural network. This scheme is meant to help the urologists in obtaining a diagnosis for complex multi-variable diseases and to reduce painful and costly medical treatments since neurological dysfunctions are difficult to diagnose. The clinical study has been carried out using medical registers of patients with urological dysfunctions. The proposal is able to distinguish and classify between ill and healthy patients.