Prediction and Classification of Rheumatoid Arthritis using Ensemble Machine Learning Approaches

Abstract

Rheumatoid arthritis (RA) is measured as an auto-immune illness that affects the musculoskeletal system causing inflammatory, systematic, and chronic effects. RA is generally progressive and diminishes the physical functionality that leads to articular and fatigue damages. Overall, RA harms bone and joint cartilage, weakening muscle joints, and destructing joints. In this investigation, medical disorder classification based on RA is done with Ensemble methods. Real-time RA data has been collected from the Sakthi Rheumatology center that holds 1000 patient profiles (750-RA affected and 250 non-affected profiles). This dataset is posed with a classification problem with numerous numerical features. Three ensemble algorithms, like SVM, Ada-boosting, and random sub-space, were used in this investigation. These ensemble classifiers use k-NN and Random forest for baseline measurements of the classifier. Data classification is performed with 10-fold cross-validation, in which evaluation is done with performance metrics like Accuracy, Precision, and ROC. The values of these metrics were compared with baseline algorithms and various ensemble classifiers. This optimality specifies the efficiency of base classifiers with ensemble classifiers, which provides substantial improvement.