Classification of Cancer Recurrence with Alpha-Beta BAM

María Elena Acevedo, Marco Antonio Acevedo and Federico Felipe

Mathematical Problems in Engineering, 2009, vol. 2009, 1-14

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%.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:680212

DOI: 10.1155/2009/680212

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