Classification of Cancer Recurrence with Alpha‐Beta BAM
Elena Acevedo, Antonio Acevedo, Marco Antonio Acevedo, Antonio Acevedo, Federico Felipe · Mathematical Problems in Engineering · 2009
Bidirectional Associative Memories (BAMs) based on first model proposed by Kosko do not have perfect recall of training set, and their algorithm must iterate until it reaches a stable state. In this work, we use the model of Alpha‐Beta BAM to classify automatically cancer recurrence in female patients with a previous breast cancer surgery. Alpha‐Beta BAM presents perfect recall of all the training patterns and it has a one‐shot algorithm; these advantages make to Alpha‐Beta BAM a suitable tool for classification. We use data from Haberman database, and leave‐one‐out algorithm was applied to analyze the performance of our model as classifier. We obtain a percentage of classification of 99.98%.