Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines

Data mining is a very popular technique and has been widely applied in different areas these days. The artificial neural network has become a very popular alternative in prediction and classification tasks due to its associated memory characteristics and generalization capability. However. the relative importance of potential input variables and the long training process have often been criticized and hence limited its application in handling classification problems. The objective of the proposed study is to explore the performance of data classification by integrating artificial neural networks with the multivariate adaptive regression splines (MARS) approach. The rationale under the analyses is firstly to use MARS in modeling the classification problem, then the obtained significant variables are used as the input variables of the designed neural networks model. To demonstrate the inclusion of the obtained important variables from MARS would improve the classification accuracy of the networks, diagnostic tasks are performed on one fine needle aspiration cytology breast cancer data set. As the results reveal. the proposed integrated approach outperforms the results using discriminant analysis, artificial neural networks and multivariate adaptive regression splines and hence provides an efficient alternative in handling breast cancer diagnostic problems. (C) 2003 Elsevier Ltd. All rights reserved.