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Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Author(s): Haowen You | George Rumbe

Journal: International Journal of Interactive Multimedia and Artificial Intelligence
ISSN 1989-1660

Volume: 1;
Issue: 3;
Start page: 5;
Date: 2010;
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Keywords: Artificial Neural Networks | Breast cancer diagnosis | Classification

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem.

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