Classification of cancer recurrence with alpha-beta BAM

María Elena Acevedo; Marco Antonio Acevedo; Federico Felipe

doi:10.1155/2009/680212

Classification of cancer recurrence with alpha-beta BAM

María Elena Acevedo, Marco Antonio Acevedo, Federico Felipe

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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.

Original language	English
Article number	680212
Journal	Mathematical Problems in Engineering
Volume	2009
DOIs	https://doi.org/10.1155/2009/680212
State	Published - 2009
Externally published	Yes

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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.",

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Classification of cancer recurrence with alpha-beta BAM

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