ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1614631
This article is part of the Research TopicNovel Biomarkers for Early Diagnosis, involved in Autoimmune and Autoinflammatory DiseasesView all 14 articles
Machine learning differentiation of rheumatoid arthritis-Sjögren's syndrome overlap from Sjögren's syndrome with polyarthritis
Provisionally accepted
- Ningbo Second Hospital, Ningbo, China
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Objective: This study aimed to evaluate the utility of machine learning algorithms in differentiating rheumatoid arthritis-Sjögren's syndrome overlap (RA-SS) from Sjögren's syndrome with polyarthritis (SS-PA), and to identify key factors influencing diagnostic differentiation. 1 Methods: This retrospective analysis included 106 RA-SS and 135 SS-PA patients randomized 7:3 into training and validation sets. Clinical, laboratory, and radiographic data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated feature selection before constructing diagnostic models using four machine learning algorithms, with feature importance quantified through SHapley Additive exPlanations (SHAP). Results: The random forest algorithm demonstrated superior performance (AUC=0.854, 95% CI: 0.747-0.944) compared to other machine learning algorithms. SHAP analysis identified anti-CCP level, rheumatoid factor (RF) level, erosive joint count, anti-SSA/Ro60 antibodies, and C-reactive protein (CRP) as critical discriminating factors between RA-SS and SS-PA. Conclusion: The random forest algorithm demonstrates promising clinical potential for RA-SS and SS-PA differential diagnosis, with diagnostic efficiency surpassing traditional logistic regression (LR), offering a new approach for clinical differentiation.
Keywords: Rheumatoid arthritis, Sjögren's syndrome, machine learning, random forest, differential diagnosis, Autoimmune diseases; Arthritis
Received: 19 Apr 2025; Accepted: 24 Jun 2025.
Copyright: © 2025 Gan, Peng, Ying, Zhang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yong Chen, Ningbo Second Hospital, Ningbo, China
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