Mathematical Problems in Engineering
Volume 2009 (2009), Article ID 680212, 14 pages
doi:10.1155/2009/680212
Research Article

Classification of Cancer Recurrence with Alpha-Beta BAM

María Elena Acevedo, Marco Antonio Acevedo, and Federico Felipe

Department of Communications and Electronic Engineering, Superior School of Mechanical and Electrical Engineering, Av. IPN s/n, Col. Lindavista, 07738 Mexico City, Mexico

Received 3 June 2009; Accepted 30 July 2009

Academic Editor: Carlo Cattani

Copyright © 2009 María Elena Acevedo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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%.