Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.

Early and accurate prediction of rheumatoid arthritis (RA) is critical for improving patient prognosis; however, existing approaches rely excessively on single autoantibody markers, neglect the systematic predictive value of routine hematological parameters, and lack mechanistic interpretability. In this study, 500 patients attending a rheumatology outpatient clinic were enrolled, and 29 routine laboratory features were collected. Anti-CCP positivity and early RA onset within 12 months were defined as dual binary prediction targets. Five traditional machine learning models (logistic regression, random forest, gradient boosting, SVM, and KNN) and five deep learning models (MLP, ResNet, Transformer, AE-Classifier, and TCN) were systematically compared using six evaluation metrics: accuracy, AUC-ROC, F1-score, precision, recall, and Matthews correlation coefficient (MCC). A four-dimensional SHAP explainability analysis was subsequently applied to the best-performing deep learning model. Logistic regression achieved the best overall performance (Accuracy = 0.848, AUC = 0.857, F1 = 0.910, MCC = 0.441). Among deep learning models, the Transformer performed relatively well (AUC = 0.812), whereas ResNet and TCN exhibited severe class collapse (MCC ≈ 0). SHAP analysis identified ESR (Mean|SHAP| = 0.097) and CRP (0.090) as the most important positive predictive drivers, and albumin (ALB, 0.062) as the key protective factor, together forming a core biomarker triad for early RA risk prediction. Dependence plots further revealed the synergistic interaction between ESR and CRP, as well as a non-linear protective threshold effect of ALB. Individual waterfall plots confirmed close alignment between model decisions and clinical pathological mechanisms. The proposed machine learning pipeline based on routine laboratory parameters can effectively predict early RA risk, and the SHAP explainability analysis transforms the model "black box" into clinically readable decision rationale, providing evidence-based support for optimizing early screening strategies in rheumatology.