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Classification of Cancer Recurrence with Alpha-Beta BAM

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  • María Elena Acevedo
  • Marco Antonio Acevedo
  • Federico Felipe

Abstract

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%.

Suggested Citation

  • María Elena Acevedo & Marco Antonio Acevedo & Federico Felipe, 2009. "Classification of Cancer Recurrence with Alpha-Beta BAM," Mathematical Problems in Engineering, Hindawi, vol. 2009, pages 1-14, October.
    • Handle: RePEc:hin:jnlmpe:680212
    DOI: 10.1155/2009/680212
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