Predicting breast cancer survivability using data mining techniques

In this paper, appropriate and efficient networks for breast cancer knowledge discovery from clinically collected data sets are investigated. Invoking various data mining techniques, it is desired to find out the percentage of disease development, using the developed network. The results, help in choosing a reasonable treatment of the patient. Several neural network structures are evaluated for this investigation. The performance of the statistical neural network structures, self organizing map(SOM), radial basis function network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are tested both on the Wisconsin breast cancer data (WBCD) and on the Shiraz Namazi Hospital breast cancer data (NHBCD). To overcome the problem of high dimension of the data set and realizing the correlated nature of the data, principal component techniques are used to reduce the dimension of data and find appropriate networks. The results are quite satisfactory while presenting a comparison of effectiveness each proposed network for such problems.

[1]  Jose A. Romagnoli,et al.  Application of wavelet-based neural networks to the modelling and optimisation of an experimental distillation column , 1997 .

[2]  V. Zitko,et al.  Principal component analysis in the evaluation of environmental data , 1994 .

[3]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[4]  Paul Gray,et al.  Introduction to Data Mining and Knowledge Discovery , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[5]  Tulay Yildirim,et al.  BREAST CANCER DIAGNOSIS USING STATISTICAL NEURAL NETWORKS , 2004 .

[6]  Bogdan M. Wilamowski,et al.  Neural Network Architectures , 2011, Intelligent Systems.

[7]  B. Hankey,et al.  The surveillance, epidemiology, and end results program: a national resource. , 1999, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[8]  Erhan Guven,et al.  PREDICTING BREAST CANCER SURVIVABILITY USING DATA MINING TECHNIQUES , 2006 .

[9]  Krzysztof J. Cios,et al.  Uniqueness of medical data mining , 2002, Artif. Intell. Medicine.

[10]  Witold Pedrycz,et al.  Neural Network Architectures , 2008, Wiley Encyclopedia of Computer Science and Engineering.

[11]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[12]  B. Hankey,et al.  Surveillance, Epidemiology, and End Results Program , 1999 .