Abstract
Data from patients after thoracic surgery caused by lung cancer are analyzed by Self Organizing Maps. Models obtained after training of these neural networks develop a clustering on synaptic weights, using k-means algorithms. Nonlinear relationships were found between patients with diseases and input variables. Results show how these models are useful for extracting value information in biomedical applications.
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© 2015 Springer International Publishing Switzerland
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Orjuela-Cañón, A.D., Gómez-Cajas, D.F. (2015). Thoracic Surgery Patients Data Analysis Using SOM Neural Networks. In: Braidot, A., Hadad, A. (eds) VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. IFMBE Proceedings, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-13117-7_194
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DOI: https://doi.org/10.1007/978-3-319-13117-7_194
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13116-0
Online ISBN: 978-3-319-13117-7
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