Efficiency of Decision Tree Algorithm For Lung Cancer CT-Scan Images Comparing With SVM Algorithm

Abstract

Machine Learning [ML] based classification algorithms play a vital role in the process of lung cancer classification based on CT images. A total of 265 CT scan images of lung cancer patients are collected and classified into samples of training dataset (n = 185 [70%]) and test dataset (n = 80 [30%]). Decision Tree and SVM algorithms are used in the process of classification, and for implementation Weka tool is used. The proposed decision tree and SVM based Lung cancer CT scan image classification has achieved an accuracy of about 99% and 97% respectively. The sample independent T-Test result of (p >0.005) and the G-power estimate of 0.8 satisfy the decision tree and SVM algorithms statistically.