Sequential minimal optimization for support vector machine with feature selection in breast cancer diagnosis

Accurate and proper diagnosis in shorter time avoids the breast cancer death. The goal is to find breast cancer as early as possible because earlier staging of breast cancer is curable. Support Vector Machine is a useful classifier among other method but the main disadvantage of Support Vector Machine (SVM) is that it´s time-consuming to train large dataset because of the traditional Quadratic Programming (QP) optimization problem. In this paper; Sequential Minimal Optimization (SMO) for SVM with feature selection in breast cancer diagnosis has been proposed. This method is more efficient on diagnosis that increases the classification accuracy with faster training time to train the datasets. We have done experiments on different training-test sets of the Wisconsin breast cancer dataset (WBCD) which is the most popular dataset among the researchers for breast cancer diagnosis. After that, performance evaluation is measured which shows the diagnostic performance of the SVM. At last, proposed approach obtained 100% accuracy with faster training time and there is no misclassification sample because false positive (FP) and false negative (FN) is zero for the model 4 in 80-20% training-test dataset.