<![CDATA[This study developed the Smart Early Detection Rheumatoid Arthritis (SEDRA) tool, designed to diagnose RA at an early stage by analyzing nail conditions. Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects joints, commonly in older individuals. Left untreated, RA can lead to severe complications such as pain, fatigue, paralysis, and even death. Early detection is essential to mitigate these effects. The research utilized advanced image processing techniques, MATLAB, Python, and a certainty factor approach. The experimental method involved capturing nail images, which were then processed in MATLAB to identify abnormalities associated with RA. Key nail indicators, including yellowing, brittleness, bloody splinters, textured surfaces, and jagged or perforated patterns, were validated using certainty factor technology to ensure diagnostic accuracy. The findings indicate that SEDRA effectively identifies RA through these nail features, providing accurate and timely diagnostic results. The results showed that this tool can detect Rheumatoid Arthritis disease through yellowing, brittle nails, bloody splinters, textured nails, and jagged or perforated nails. SEDRA was created to meet the needs of innovation in the health sector. SEDRA represents a breakthrough in health technology, providing a practical tool for early RA detection that can be integrated into primary healthcare systems. Its implications include improving patient outcomes by enabling early intervention and monitoring. Future research should focus on enhancing the diagnostic accuracy of SEDRA, expanding its applicability to diverse populations, and integrating it with mobile or wearable technologies to increase accessibility and usability in remote or underserved areas.]]>
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