Abstract
Machine learning approaches were employed for malignant breast tumour diagnosis and evaluation of the prognostic risk of recrudescence and metastasis by using age and ten cellular attributes of Fine Needle Aspirate of Breast (FNAB) and gene microarrays data of the breast cancer patient respectively. Feature ranking method was introduced to explore the salient elements for cancer identification and simultaneous improve the classification accuracy. In this paper, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Probabilistic Neural Network (PNN) combined with Signal-to-Noise Ratio (SNR) for feature ranking and filtering were applied to distinguish between the benign and malignant tumours of breast and evaluate the prognostic risk of recrudescence and metastasis. The results reveal that feature ranking method SNR can effectively pick out the informative and important features, which had significance for clinical assistant diagnosis and is useful for improving the performance of evaluation. The best overall accuracy for breast cancer diagnosis and evaluating the prognostic risk of recrudescence and metastasis achieved 96.24% and 88.81% respectively, by using SVM-Sigmoid and SVM-RBF combined with SNR under 5-fold cross validation. This study suggests that SVM may be further developed to be a practical methodology for clinical assistant differentiating between benign and malignant tumours and possible to help the inexperienced physicians avoid misdiagnosis. It also has benefit to the cured patients who are predicted as recrudescence and metastasis pay more attention to their diseases, and then reduce the mortality rate of breast cancer.
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Yuan, Q., Cai, C., Xiao, H., Liu, X., Wen, Y. (2007). Diagnosis of Breast Tumours and Evaluation of Prognostic Risk by Using Machine Learning Approaches. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_141
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DOI: https://doi.org/10.1007/978-3-540-74282-1_141
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