Authors:
David Gil Mendez
1
;
Magnus Johnsson
2
;
Antonio Soriano Paya
1
and
Daniel Ruiz Fernandez
1
Affiliations:
1
Computing Technology and Data Processing, University of Alicante, Spain
;
2
Lund University, Sweden
Keyword(s):
Artificial neural networks, urology, artificial intelligence, medical diagnosis, decision support systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Decision Support Systems
;
Expert Systems
;
Health Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
In this article we evaluate the work out of artificial neural networks as tools for helping and support in the medical diagnosis. In particular we compare the usability of one supervised and two unsupervised neural network architectures for medical diagnoses of lower urinary tract dysfunctions. The purpose is to develop a system that aid urologists in obtaining diagnoses, which will yield improved diagnostic accuracy and lower medical treatment costs. The clinical study has been carried out using the medical registers of patients with dysfunctions in the lower urinary tract. The current system is able to distinguish and classify dysfunctions as areflexia, hyperreflexia, obstruction of the lower urinary tract and patients free from dysfunction.