Revolutionizing Cancer Diagnosis: Unleashing the Tab Transformer's Power for Accurate Classification

Abstract

Gene expression platforms offer vast amounts of data that can be utilized for investigating diverse biological processes. However, the presence of redundant and irrelevant genes makes it challenging to identify crucial genes from high-dimensional biological data. To overcome this obstacle, researchers have introduced different feature selection (FS) methods. Developing more efficient and accurate feature selection techniques is essential for selecting important genes in complex biological information with multiple dimensions. In this context, we introduce an innovative method in the field of cancer diagnosis through the utilization of the TabTransformer model. With an unparalleled accuracy of 98%, achieved across diverse cancer datasets encompassing colon, lung, and ovarian cancers, the TabTransformer demonstrates its transformative potential in accurate cancer classification. Leveraging its innovative architecture and advanced learning capabilities, the model excels in distinguishing between cancerous and non-cancerous instances. The remarkable accuracy achieved in colon cancer classification underscores the model's precision in differentiating complex tissue compositions. In lung cancer classification, the TabTransformer’ s ability to identify intricate cancer-related patterns further solidifies its effectiveness. Additionally, its proficiency in handling the intricacies of ovarian cancer data underscores its versatility. This study showcases the TabTransformer's ability to drive the future of cancer diagnosis, offering a path toward enhanced medical research, treatment, and patient outcomes.