Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

TitleComparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data
Publication TypeJournal Article
Year of Publication2010
AuthorsYouh, H., and G. Rumbe
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueA Direct Path to Intelligent Tools
Volume1
Number3
Date Published12/2010
Pagination5-12
Abstract

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.

KeywordsArtificial Neural Networks, Classification
DOI10.9781/ijimai.2010.131
URLhttp://www.ijimai.org/journal/sites/default/files/IJIMAI20101_3_1.pdf
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IJIMAI20101_3_1.pdf709.37 KB