Machine Learning and Deep Learning Techniques for Detection of Rheumatoid Arthritis: A Comprehensive Survey

Rheumatoid Arthritis (RA) poses a significant health challenge worldwide, necessitating precise and efficient detection methods. This survey explores the landscape of RA detection, recognizing its pivotal role in alleviating the burden of this chronic autoimmune ailment. By employing machine learning techniques such as Decision Trees, Support Vector Machines, Naive Bayes, and deep learning algorithms, this study examines various approaches, each with its distinct advantages and challenges. Recent advancements in RA detection research and evaluations of state-of-the-art methodologies' effectiveness are emphasized. Additionally, the paper discusses evolving hurdles in RA detection and suggests future research directions to address them. Ultimately, the refinement of robust detection techniques is crucial for early intervention and improved management of Rheumatoid Arthritis, thereby enhancing the quality of life for affected individuals.