Enhanced Prediction of Rheumatoid Arthritis Flares Through Ensemble Learning Methods

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective.