Breast Cancer Survivability via AdaBoost Algorithms

Thongkam, J., Xu, G., Zhang, Y. and Huang, F.

The use of data mining in medical domains is increasing rapidly. This is mainly because the effectiveness of these approaches to classification and prediction has imporved, particularly in relation to helping medical practitioners in their decision making. This type of research has become important for finding ways to improve patient outcomes, reduce the cost of medicine, and further advance clinical studies. Therefore, in this paper, data pre-processing RELIEF attributes selection, and Modest AdaBoost algorithms, are used to extract knowledge from the breast cancer survival database in Thailand. The performance of these algorithms is examined by using classification accuracy, sensitivity and specificity, confusion matrix and stratified 10-fold cross-validation method. Computational results showed that Modest AdaBoost outperforms real and Gentle AdaBoosts.

Cite as: Thongkam, J., Xu, G., Zhang, Y. and Huang, F. (2008). Breast Cancer Survivability via AdaBoost Algorithms. In Proc. Second Australasian Workshop on Health Data and Knowledge Management (HDKM 2008), Wollongong, NSW, Australia. CRPIT, 80. Warren, J. R., Yu, P., Yearwood, J. and Patrick, J. D., Eds. ACS. 55-64.

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