Abstract
The article presents application of artificial immune algorithms in classification of vectorized medical data sets. Artificial immune network was created and trained for the purpose of arterial blood gasometry parameters (pH, pCO2, pO2, HCO3) classification. Training data originates from the Infant Intensive Care Unit of the Polish – American Institute of Pediatry, Collegium Medicum, Jagiellonian University in Cracow.
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Keywords
- Artificial Immune System
- Immune Network
- Pattern Series
- Clonal Selection Algorithm
- Unsupervised Pattern Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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Wiesław, W., Piotr, W.: Autonomous Agent for Computer System and Computerized System of Automatics, Some Analogy and Difference. In: International Workshop Control and Information Technology, IWCIT 2001 (2001)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wajs, W., Wais, P., Święcicki, M., Wojtowicz, H. (2005). Artificial Immune System for Medical Data Classification. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428862_112
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DOI: https://doi.org/10.1007/11428862_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26044-8
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