Dr. M.Mayilvaganan et al, International Journal of Computer Science and Mobile Computing, Vol.8 Issue.7, July- 2019, pg. 88-97 © 2019, IJCSMC All Rights Reserved 88 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 6.199 IJCSMC, Vol. 8, Issue. 7, July 2019, pg.88 – 97 PERFORMANCE COMPARISION OF DATA CLASSIFICATION ALGORITHM FOR ANALYSIS LUNG CANCER DATASETS Dr. M.Mayilvaganan 1 ; N.Thamaraikannan 2 Associate Professor, Department of Computer Science, PSG College of Arts and Science, Coimbatore, India 1 Email Id – mayil24_02@yahoo.co.in Research Scholar, Department of Computer Science, PSG College of Arts and Science, Coimbatore, India 2 Email Id – n.thamaraikannan57@gmail.com ABSTRACT: Lung cancer is the major cause of cancer deaths in both men and women in world wide. Lung malignancy begins when cells of the lung begin unusual cells grow into the lungs. Various types of approaches have been used for lung cancer diagnosis. There are two kinds of lung cancer that is small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC).People who smoke have greatest risk of carcinoma. The risk of carcinoma will increase with the length of your time and variety of cigarettes they need preserved. To gather the information and to measure the data’s based on smokers, Nonsmokers in keeping with their age and history of habit. In this research works mainly specialized in lung cancer data and it uses machine learning techniques Naive bayes and decision tree. The main objective of this technique used for classification and prediction and mainly focused on performance comparison of machine learning algorithm with respective run time execution of each algorithm. The naïve bayes and decision tree algorithm used to analysis for performance comparison accuracy of each algorithm. We were using the Rapid Miner software to predict the accuracy of Classification algorithms. In this comparison we use Lift Chart and ROC curve in which it displays the accurate value of classification.