Predicting breast cancer survivability using data mining techniques

Abstract

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.

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