Abstract
In this study, we designed an intelligent system to examine the influence of the demographic variables of gender, blood type, and race on the distribution of skin staphylococci bacteria on neonates up to 48 hours old. Bacterial samples were obtained from the axilla and groin of 200 babies, born over an 18-month period, and the staphylococci therein identified using standard microbiological techniques. The intelligent system created for the analysis of this real database was a supervised neural network program made up of a Multi-Layer Perceptron. Six skin staphylococcal species could be reproducibly classified, namely S. aureus (95.2%), S. saprophyticus (88.9%), S. hominis (84.2%), Micrococcus sp. (83.3%), S. haemolyticus (77.9%) and S. epidermidis (77.7%). The remaining three species, which constituted less than 3% of all isolates, were insufficient in number to assign to a class. Of the variables investigated, site of isolation had no effect on determining the staphylococcal distribution and the demographic variables, in order of importance, were found to be gender, blood type and race. These studies suggest that demographic variables are major factors influencing staphylococcal distribution. Such a system may, therefore, hold promise as a diagnostic tool.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cundell D.R., Silibovsky R.S., Sanders R. and Sztandera L.M. (2000) Analyzing putative correlates between age, blood type, gender and/or race with bacterial infection. Artificial Intelligence in Medicine 586: 1–5
Cundell D.R., Silibovsky R.S., Sanders R. and Sztandera L.M. (2001) An Intelligent Medical System to diagnose bacterial infection in hospitalized patients. International Journal of Medical Informatics 63: 31–40
Sztandera L. M. and Cios K. J. (1997), Ontogenic Neuro-Fuzzy Algorithm: F-CID3, Neurocomputing, 14: 383–402.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cundell, D.R., Sztandera, L.M., Arbeter, A., Morrone, J.M. (2003). An Investigation of Differential Skin Colonization of Neonates by Staphylococci, Using an Artificial Neural Network-based System. 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_71
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1902-1_71
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
eBook Packages: Springer Book Archive