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Feature Space Optimization in Breast Cancer Diagnosis Using Linear Vector Quantization

Author(s): A. Punitha | T. Santhanam

Journal: Information Technology Journal
ISSN 1812-5638

Volume: 6;
Issue: 8;
Start page: 1258;
Date: 2007;
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Keywords: Correlation | learning vector quantization network | principal component analysis | relevance and redundancy

One of the major challenges in medical domain is the extraction of intelligible knowledge from medical diagnosis data. It is quite common among the researching community to apply Principal Component Analysis (PCA) for the extraction of prominent features and to use feature correlation method for redundant features removal. This paper discusses a three-phase approach selection technique to extract features for further usage in clinical practice for better understanding and prevention of superfluous medical events. In the first phase PCA is employed to extract the relevant features followed by the elimination of redundant features using the class correlation and feature correlation technique in phase two and in the final phase Learning Vector Quantization (LVQ) network is utilized for classification. The proposed method is validated upon Wisconsin Breast Cancer Database (WBCD), which is a very well known dataset obtained from the UCI machine-learning repository. The abridged feature set and classification accuracy are found to be satisfactory.
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