Breast cancer diagnosis using least square support vector machine

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system. (c) 2006 Elsevier Inc. All rights reserved.