On Improving Breast Cancer Prediction Using GRNN-CP

Authors: Kefaya Qaddoum

Abstract:

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: Neural network, conformal prediction, cancer classification, regression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132244

References:

[1] H. Papadopoulos, “Regression conformal prediction with nearest neighbours,” J. Artificial Intelligence Res., vol. 40, pp.815–840, Jan 2011.
[2] D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Netw., vol. 2, no. 6, pp. 568–576, Nov. 1991.
[3] Umesh D R, B Ramachandra, "Association rule mining based predicting breast cancer recurrence on SEER breast cancer data", Emerging Research in Electronics Computer Science and Technology (ICERECT) 2015 International Conference on, pp. 376-380, 2015.
[4] H. Holst, M. Ohlsson, C. Peterson, L. Edenbrandt, "Intelligent computer reporting ‘lack of experience’: a confidence measure for decision support systems", Clinical Physiology, pp. 139-147, 1998.
[5] H. Papadopoulos, A. Gammerman, V. Vovk, "Confidence Predictions for the Diagnosis of Acute Abdominal Pain", Proc. 5th IFIP Conference on Artificial Intelligence Applications & Innovations, pp. 175-184, 2009.
[6] A. Gammerman, I. Nouretdinov, B. Burford, A. Chervonenkis, V. Vovk, Z. Luo, "Clinical mass spectrometry proteomic diagnosis by conformal predictors", Statistical applications in genetics and molecular biology, vol. 7, no. 2, 2008.
[7] A. Asuncion, D.J. Newman, UCI Machine Learning Repository, Irvine, CA:University of California, School of Information and Computer Science.
[8] O. L. Mangasarian, W. H. Wolberg, "Cancer diagnosis via linear programming", SIAM News, vol. 23, no. 5, pp. 1-18, 1990.
[9] Kefaya Qaddoum, Evor Hines, and Daciana Iliescu, yield prediction technique using hybrid adaptive neural genetic network, Int. J. Comp. Intel. Appl. 11, 1250021 (2012) (15 pages) DOI: http://dx.doi.org/10.1142/S1469026812500216.
[10] A general regression neural network, D. F. Specht, October 1991 IEEE Transactions on Neural Networks: Volume 2 Issue 6, November 1991.