DOI
10.9781/ijimai.2010.131
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.
Source Publication
International Journal of Interactive Multimedia and Artificial Intelligence
Recommended Citation
Rumbe, G and Youh, Haowen
(2010)
"Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data,"
International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 1:
Iss.
3, Article 10.
DOI: 10.9781/ijimai.2010.131
Available at:
https://ijimai.researchcommons.org/ijimai/vol1/iss3/10
