ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD

Abstract

The International Agency for Research on Cancer (IARC) revealed staggering figures, with 19.3 million global cancer cases and 10 million related deaths in that year. Cancer, characterized by abnormal cell growth, can potentially be dangerous with the ability to metastasize. Notably, lung cancer is often detected in an advanced stage due to a lack of awareness and comprehensive medical assessment. Lung cancer usually presents with a late-stage diagnosis. From 60% to 85% of individuals diagnosed with lung cancer show a lack of awareness about their condition. Early diagnosis using an accurate classification method can significantly increase the success of lung cancer diagnosis. To improve predictions, Decision Tree Classifier method was used in lung cancer classification, resulting in a significant increase in accuracy. This study achieved a good level of accuracy, with an accuracy value of 95.16% at a max_depth model depth of 15, and tested in 40 experimental iterations. These results are expected to provide hope for progress in the classification of lung cancer. Keywords: Lung, Cancer, Classification, Decision Tree