Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Author
Keywords
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.
Year of Publication
2010
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
1
Issue
A Direct Path to Intelligent Tools
Number
3
Number of Pages
5-12
Date Published
12/2010
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/IJIMAI20101_3_1.pdf
DOI
10.9781/ijimai.2010.131
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