Polat, KemalGuenes, Salih2020-03-262020-03-2620071051-2004https://dx.doi.org/10.1016/j.dsp.2006.10.008https://hdl.handle.net/20.500.12395/21250The 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.en10.1016/j.dsp.2006.10.008info:eu-repo/semantics/closedAccessbreast cancer diagnosisWisconsin breast cancer diagnosis dataleast square support vector machineconfusion matrixk-fold cross validationmedical diagnosisArticle174694701Q2WOS:000247899300002Q2