인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Rheumatoid arthritis (RA) is a chronic disease that causes irreversible joint damage. Early detection, especially in primary care settings, is crucial for effective disease management. This study aimed to identify the factors that help screen individuals at risk of RA to reduce delays in referral to rheumatologists. This analytical and applied research used a questionnaire to gather data from 377 patients at a rheumatology diagnostic center in Ahvaz, Iran, between August and November 2024. Study variables included patients' articular and extra-articular symptoms at disease onset, demographic data, and initial laboratory markers. After performing statistical correlation analysis, the dataset was split into training (80%) and testing (20%) subsets. Five machine learning models were developed, and the SHAP method was applied to the best-performing model to identify influential features. The results were obtained via 5-fold nested cross-validation, which identified the CatBoost model as the top performer, with AUC-ROC = 0.966, Accuracy = 0.947, and F1-Score = 0.951. SHAP (with a threshold of 0.01) highlighted the following significant features: Anti-CCP, tender joint count, swollen joint count, gastrointestinal issues, fatigue, age, RF (Rheumatoid Factor), and hearing problems. Due to the importance of early RA diagnosis and the challenges encountered in primary care, three main screening factors stand out: Anti-CCP, tender joint count, and swollen joint count. These, along with fatigue, age, and positive RF, markedly increase the likelihood of RA and justify referring a patient to a specialist.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
