  In this paper, we study the application of GIST SVM in disease prediction
(detection of cancer). Pattern classification problems can be effectively
solved by Support vector machines. Here we propose a classifier which can
differentiate patients having benign and malignant cancer cells. To improve the
accuracy of classification, we propose to determine the optimal size of the
training set and perform feature selection. To find the optimal size of the
training set, different sizes of training sets are experimented and the one
with highest classification rate is selected. The optimal features are selected
through their F-Scores.
