Clinical Text Classification Using Sequential Forward Selection

Clinical text classification is a critical undertaking within the realm of Natural Language Processing, holding significant implications for healthcare applications. In this NLP project, our primary objective is the categorization of medical transcripts into their corresponding medical conditions. To accomplish this, we harness the power of Sequential Forward Selection (SFS), a feature selection technique meticulously chosen for its capacity to streamline data dimensions. By employing SFS, our project aspires to not only enhance classification performance but also elevate the efficiency of pattern recognition, ensuring both speed and accuracy in disease identification. This research contribution underscores the pivotal role of Clinical Text Classification, with a specific focus on optimizing the process using SFS.