Lung Cancer Classification and Prediction Based on Statistical Feature Selection Method Using Data Mining Techniques

Now a days, cancer effect is dangerous issues facing everywhere throughout the world. Doctors can predict the lung cancer when anomalous cells raise and grow in an uncontrolled way in lungs. To overcome this issue, researchers in medical image processing are fully automating it and the medical industry is also automating itself. Resulting of cancer death rate is more as the cancer diagnose happened at the last stage of the cancer. The proposed research paper endeavours to check accuracy ratio of three classifiers which is Zero-R, JRIP rules, and Decision table to find lung cancer in its early stages with the aim of save lives. For the proposed methodology, used a dataset from the UCI data repository as cancer dataset Lung cancer. This paper's main focus is on the execution examination of WEKA tool's classification algorithms for accuracy. The main goal of this study is to find early detection of lung cancer using machine learning algorithms. After evaluating the performance of the algorithms shows that Decision table best used for prediction of lungcancer.