Mining Lung Cancer Data for Smokers and Non-Smokers by Using Data Mining Techniques

Abstract: Lung cancer is the uncontrolled growth of abnormal cells that start off in one or both lungs usually in the cells that line the air passages. Lung cancer is the leading cause of cancer deaths in the United States, among both men and women. The two main types are small cell lung cancer and non-small cell lung cancer. These types are diagnosed based on how the cells look under a microscope. People who smoke have the greatest risk of lung cancer. The risk of lung cancer increases with the length of time and number of cigarettes they have smoked. If they quit smoking, even after smoking for many years, they can significantly reduce his/her chances of developing lung cancer. In this work, we apply classification techniques on a dataset of lung cancer patients based on smoking and non-smoking people.

Keywords: Data mining, lung cancer predication, classification, Decision Table, Naive Bayed, lymphoma, pulmonary carcinoma.