Automated Diagnostic System for Breast Cancer Using Least Square Support Vector Machine
Hamid Fiuji, Behnaz Naghash Almasi, Zahra Mehdikhan, Bahram Bibak, Mohammad Taher Pilevar, Omid Naghash Almasi · 2013
Breast cancer is currently going to be one of the leading causes of death among women all over the world; however, it is for sure that the early detection and accurate diagnosis of this type of cancer can assure a longer survival of the patients. Because of the effective classification and high diagnostic capability, expert systems and machine learning techniques are now gaining popularity in this field. In this study, Least square support vector machine (LS-SVM) was used for breast cancer diagnosis. The effectiveness of the LS-SVM is examined on Wisconsin Breast Cancer Dataset (WBCD) using K-fold cross validation method. Compared to nineteen well-known methods for the breast cancer diagnosis in the literature, the study results showed the effectiveness of the proposed method.