American Journal of Biomedical Engineering 2013, 3(6): 175-181 DOI: 10.5923/j.ajbe.20130306.06 Automated Diagnostic System for Breast Cancer Using Least Square Support Vector Machine Hamid Fiuji 1 , Behnaz N. Almasi 2 , Zahra Me hdikhan 3 , Bahram Bibak 4 , Mohammad Pilevar 5 , Omid N. Almasi 6,* 1 Department of Biochemistry, Faculty of Science, Payame Noor University, M ashhad, Iran 2 Department of Medical Science, Faculty of Nursing and Midwifery, Islamic Azad University, Mashhad, Iran 3 Department of Electrical Engineering, Islamic Azad University, M ashhad, Iran 4 Department of Molecular Science, North Khorasan University of Medical Sciences, Bojnord, Iran 5 Department of Animal Sciences, Faculty of Agriculture, Ferdowsi University, M ashhad, Iran 6 Department of Electrical Engineering, Islamic Azad University, Gonabad, Iran Abstract 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. Keywords Breast Cancer Diagnosis, K-Fold Cross Validation, Medical Diagnosis, Least Square Support Vector Machine, Wisconsin Breast Cancer Dataset 1. Introduction A leading cause of death among women between 40and 55 years of age, breast cancer is now the second major cause of death among women. According to the World Health Organization, every year more than 1.2 million women are diagnosed with breast cancer across the globe. Luckily, in recent years with an increased emphasis on diagnostic techniques and more effective treatments, the mortality rate from breast cancer has declined. A key factor in this approach is the early detection and accurate diagnosis of this affliction[1-3]. Undoubtedly, the evaluation of data taken from patients and decisions of experts are the most important factors in diagnosis. Therefore, the use of classifier systems in medical diagnosis has been gradually increasing. After all, expert systems and various artificial intelligence techniques for classification also help experts to a considerable extent. Classification systems can help minimize possible errors that might occur due to inexperienced experts, and also provide medical data to be examined in shorter time and more detailed[3, 4]. * Corresponding author: o.almasi@ieee.org (Omid N. Almasi) Published online at http://journal.sapub.org/ajbe Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved Proposed as effective statistical learning methods for classification[5], Support Vector Machines (SVMs) rely on support vectors (SV) to identify the decision boundaries between different classes. Nonlinearly related to the input space, SVM is based on a linear machine in a high dimensional feature space, which has allowed the development of somewhat quick training techniques, despite the large number of input variables and large training sets. SVMs have successfully been used to address many problems including handwritten digit recognition[6], object recognition[7], speaker identification[8], face detection in images[9], and text categorization[10]. The Least Square Support Vector Machine (LS-SVM) was first proposed by Suykens and et al. by modifying the formulation of standard SVM[11]. The LS-SVM was modified at two points: First, instead of inequality constraints, it takes equality constraints and changed the quadratic programming to a linear programming. Second, a squared loss function is taken from the error variable[11, 12]. In this study, LS-SVM was employed to diagnose the breast cancer. For training and testing experiments, WBCD taken from the University of California at Irvine (UCI) machine learning repository was used. It was observed that the proposed method yielded the highest classification accuracies among the nineteen other methods in the literature. In this study, the performance was evaluated by the well-known k-fold cross validation method.