Abstract
Many pattern recognition and machine learning methods have been used in cancer diagnosis. In this study, we propose a kernel orthogonal transform method for breast cancer diagnosis. We test our method using the widely used Wisconsin breast cancer diagnosis (WBCD) dataset. The performance of the method is evaluated in terms of the classification accuracy, specificity, positive and negative predictive values, as well as receiver-operating characteristic curve (ROC). The experimental results show that our method classifies more accurately than all of the previous methods.
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Acknowledgments
This article is partly supported by Program for New Century Excellent Talents in University (Nos. NCET-08-0156 and NCET-08-0155), NSFC under grants No. 61071179, 60803090, 60902099, and 61001037, as well as the Fundamental Research Funds for the Central Universities (HIT.NSRIF. 2009130).
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Xu, Y., Zhu, Q. & Wang, J. Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput & Applic 21, 1865–1870 (2012). https://doi.org/10.1007/s00521-011-0547-0
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DOI: https://doi.org/10.1007/s00521-011-0547-0