Predicting Lung Cancer Using Datamining Techniques With the AID of SVM Classifier

Abstract

The suggested techniques provide a noble quality tool to predict lung tumor classification and play a major role, particularly in the finding and classification of medical data. The literature reports a number of lung cancer diagnosis systems which predict normal and abnormal lung cancers with the support of SVM. Our proposed research focuses on predicting lung cancer whether it is normal or abnormal, with respect to the classification technique. Initially, in the preprocessing phase, suitable data from the input data set are extracted after preprocessing; the resultant output is fed to the feature selection. In this feature selection phase, the features are selected with the aid of the firefly algorithm. After the feature selection, the particular features are served in to the support vector machine (SVM) classifier; with the aid of this classifier, the data are classified as either normal or abnormal. The proposed method will be implemented in Matlab with various lung cancer data. In addition to this, our proposed work will be in comparison with the present strategies and algorithms for proving that our proposed work is the best one.