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.
