Advancing Cancer Document Classification with R andom Forest

Abstract


 In this study, we address the challenging task of biomedical text document classification of Cancer Doc Classification, specifically focusing on lengthy research papers related to cancer. Unlike previous research that often deals with shorter abstracts and concise summaries, we curated a unique dataset comprising documents with more extensive content, each exceeding 6 pages in length. To tackle this classification challenge, we employed the Random Forest Tree method. Random Forest is a powerful ensemble learning technique that combines multiple decision trees to enhance classification accuracy and robustness. It has been widely adopted in the field of machine learning and data science for its effectiveness in handling complex classification tasks.