Summary
Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed “artificial nose” based on chemoresistive sensors has been employed to identify in vivo urine samples from 45 patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Public Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on a hybrid system of genetic algorithms, neural networks and multivariate techniques such as principal components analysis and discriminant function analysis-cross validation. The experimental results confirm the validity of the presented method.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kodogiannis, V.S., Pavlou, A.K., Chountas, P., Turner, A.P.F. (2003). Evolutionary Computing Techniques for Diagnosis of Urinary Tract Infections in Vivo, Using Gas Sensors. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_72
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_72
Publisher Name: Physica, Heidelberg
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