Breast cancer prediction and cross validation using multilayer perceptron neural networks

Mojarad, Shirin, Dlay, Satnam, Woo, Wai Lok and Sherbet, Gajanan (2010) Breast cancer prediction and cross validation using multilayer perceptron neural networks. In: 2010 7th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2010. IEEE, pp. 760-764. ISBN 9781424488582

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The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers has prohibited an accurate prognosis. The aim of this paper is to investigate the effectiveness of a multilayer perceptron (MLP) for the aim of predicting breast cancer progression using a set of four biomarkers of breast tumours. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold cross validation (CV) are also studied in order to assess their accuracy and reliability for neural network validation. We used output accuracy, sensitivity and specificity for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Findings suggest that ANN-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed.

Item Type: Book Section
Uncontrolled Keywords: Breast cancer, k-fold cross validation, multilayer perceptron (MLP), predictive analysis
Subjects: B900 Others in Subjects allied to Medicine
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 13 May 2019 11:31
Last Modified: 10 Oct 2019 19:01

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