SVM kernel Methods with Data Normalization for Lung Cancer Survivability Prediction Application

Cancer is a threatening disease for the human race affecting most people around the world. The topmost reason for cancer demise across the globe is lung cancer and therefore there were many algorithms applied in predicting the survival rate of lung cancer patients. As a result of which the survival rate of lung cancer patients is increasing gradually. Support Vector Machine (SVM) technique have high accuracy than other technique in prediction. The performance of the SVM algorithm depends on the kernel function. In this paper, a comparison of three different kernel functions predicting the survival rate of a lung cancer patient with an efficient normalization technique is studied. Experiments are conducted in the dataset obtained from Cancer Imaging Archive (TCIA). Along with SVM kernel functions, five machine learning techniques were also used in predicting the survival rate of lung cancer. RBF_SVM with normalized data produced high accuracy of 97.72% compared to another algorithm. Various performance metrics such as accuracy, precision, recall, F1 score are used to evaluate the performance of the SVM kernel function.