Development and validation of an interpretable machine learning model for standard spleen volume prediction.

Background: Splenomegaly serves as a crucial indicator for various diseases, particularly in hepatosplenomegaly and hematological disorders. Accurate assessment of splenomegaly is essential for improving diagnostic accuracy and treatment decisions, yet individualized diagnosis necessitates a standard reference for splenic volume. This study aimed to develop an interpretable machine learning (ML) model to evaluate standard splenic volume (SSV), enhancing personalized clinical decision-making. Methods: We conducted a retrospective analysis of 1,186 volunteers from a multicenter cohort and evaluated 11 ML algorithms. SHapley Additive exPlanations (SHAP) were employed for feature selection and interpretation. Model performance was rigorously evaluated through key metrics such as root mean squared error (RMSE), coefficient of determination (R Results: Model development and internal validation involved 511 eligible volunteers, with external validation from an additional 111 volunteers. The random forest (RF) model (ML_SSV) integrating features such as age, body weight (BW), body height, body mass index (BMI), body surface area (BSA), red blood cell count, platelet count, total bilirubin, fibrinogen, and D-dimer, demonstrated exceptional predictive accuracy. In external validation, the model achieved an RMSE of 22.6 mL (R Conclusions: This study establishes a novel interpretable ML model rigorously validated through statistical and clinical benchmarks. These models enable the assessment of SSV, providing a reference baseline for the individualized diagnosis of splenomegaly to enhance diagnostic accuracy and support data-driven clinical decision-making.