Leveraging Machine Learning for Classification of Rheumatoid Arthritis: A Comparative Analysis of Classifier Performance

A systemic inflammatory illness affecting the musculoskeletal system, rheumatoid arthritis (RA) is characterized by chronic, systemic symptoms. Injuries to joints and muscles from wear and tear are expected outcomes of rheumatoid arthritis (RA)., a degenerative illness that worsens with time. Arthritis rheumatoid generally degenerates muscles and joints while damaging bone and cartilage. To categorize medical conditions according to RA, this study employs machine learning approaches. Real-time RA data from the Sakthi Rheumatology Center, which keeps track of one thousand patient profiles has been used in this study. Multiple numerical features of the dataset are used for the classification task RA. The machine learning approaches used in this experiment were support vector machine (SVM), logistic regression, SDG, kNN, and ada-boosting. Ada-boost outperforms other classifiers regarding prediction accuracy rate and generating 98.7% classification accuracy. To classify the data, 20-fold cross-validation is used, and its efficacy is evaluated with metrics like precision, recall, sensitivity, and accuracy. The values of these measures were compared to a range of classifiers and baseline techniques.