ArticleFrontiers in medicine2026
AdaBoost-based differential diagnosis of gouty arthritis and rheumatoid arthritis: model construction, temporal validation, and SHAP visualization.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The phenotypic overlap between gouty arthritis (GA) and Rheumatoid Arthritis (RA) poses diagnostic challenges, particularly in resource-limited settings. This study aims to develop a robust, non-invasive machine learning (ML) framework based on routine hematological parameters to facilitate precise differential diagnosis and to evaluate its robustness in a temporally independent cohort. Methods: Adopting a temporal validation design, we retrospectively analyzed clinical data from a tertiary hospital. A development cohort (2021-2025; Results: Six pivotal features were identified, comprising demographics, traditional markers, and novel composite ratios (CHOL/TG). AdaBoost demonstrated superior performance, achieving an AUC of 0.898 in the internal test set and an impressive 0.897 in the time-based internal validation, indicating exceptional generalization. SHAP analysis revealed that beyond conventional markers, CHOL/TG is a key predictor, revealing a unique interaction pattern of lipid metabolism that distinguishes gouty arthritis (GA) from rheumatoid arthritis (RA). Conclusion: We successfully developed and validated an AdaBoost-based model leveraging routine blood indices for precise GA/RA differentiation. Validated across temporal cohorts, this cost-effective tool holds significant promise for optimizing early triage in primary care, while offering novel pathophysiological insights through the lens of lipid metabolism.
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