Construction and validation of a differential diagnosis model for multiple myeloma and lymphoma based on XGBoost machine learning: an explaining analysis study of SHAP

Multiple myeloma (MM) and lymphoma can share overlapping clinical features but require different diagnostic and treatment strategies. Machine learning (ML) may identify discriminative patterns from routinely available clinical data and support rapid triage. This retrospective study included patients from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine between 2015 and 2025. Data from 2015 to 2024 ( n = 2,759) were used for model development, and the 2025 cohort ( n = 1,011) served as an independent temporal external validation set. Feature selection combined univariate analysis, collinearity assessment, extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and Boruta, yielding 14 core features. Nine ML models were compared. XGBoost showed the best validation performance, with an area under the receiver operating characteristic curve (AUC) of 0.964, accuracy of 0.904, sensitivity of 0.913, specificity of 0.896, positive predictive value of 0.889, and negative predictive value (NPV) of 0.918. In the held-out internal test set, the final model achieved an AUC of 0.947 and an accuracy of 0.890. External validation yielded an AUC of 0.713, sensitivity of 0.400, specificity of 0.768, and NPV of 0.876. Shapley Additive Explanations (SHAP) identified age, mean corpuscular hemoglobin, red cell distribution width coefficient of variation, platelet-to-lymphocyte ratio, and mean corpuscular volume as the leading contributors to model predictions. The XGBoost model demonstrated good discriminatory ability in distinguishing multiple myeloma from lymphoma by integrating 14 routinely available variables: age, WBC, MONO, EO, BASO%, RBC, HGB, HCT, MCV, MCH, RDW-CV, PLT, LYMPH/MONO, and PLT/LYMPH. This multivariable framework may serve as an adjunctive tool to support the differential diagnosis of these two hematological malignancies.