International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Rheumatoid Arthritis (RA) is a chronic autoimmune disease that causes progressive joint inflammation and irreversible structural damage if not diagnosed at an early stage. Accurate classification of RA subtypes, such as seropositive and seronegative RA, along with the identification of erosive joint changes from radiographic images, is essential for effective clinical decision-making. However, conventional diagnostic approaches rely heavily on manual interpretation of laboratory biomarkers and X-ray images, which are time-consuming and subject to inter-observer variability. This work proposes an artificial intelligence–based dual-modal diagnostic framework for automated rheumatoid arthritis disease subtype classification. The system integrates numerical clinical biomarkers and hand X-ray imaging to provide complementary diagnostic insights. The numerical model utilizes six key laboratory parameters—age, gender, rheumatoid factor (RF), anti-cyclic citrullinated peptide (Anti-CCP), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR)—to classify patients into Healthy, Seropositive RA, and Seronegative RA using an XGBoost classifier. In parallel, an EfficientNet-B3 deep learning model is employed to analyze hand X-ray images for erosive and non-erosive joint damage detection. Experimental evaluation demonstrates that the numerical model achieves an accuracy of 89.28% with a ROC-AUC of 93.21%, while the imaging model attains 85.83% accuracy with a 95.04% recall for erosive cases. The proposed system is deployed as a real-time, web-based clinical decision support tool using Streamlit, providing fast and interpretable predictions. This approach highlights the effectiveness of multimodal AI systems in enhancing early RA diagnosis and subtype classification.

Keywords: Rheumatoid Arthritis, Disease Subtype Classification, Medical Imaging, Machine Learning, Deep Learning, Clinical Decision Support

Downloads: | DOI: 10.17148/IJARCCE.2025.141284