Development and validation of an interpretable machine learning model for standard spleen volume prediction
Jinyu Lin1,2,3, Jian Yang2,3, Yinling Qian4
1Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
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 (R2), and additional validation parameters, further validated through comparisons with prior published formulas. We also developed free, open-access web-based calculators for the predictive model.
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 (R2=0.80), with residual analysis confirming normally distributed errors (range: -58.32 to 67.01 mL; P=0.201). Notably, a simplified RF model (ML_SSVa) utilizing only four non-invasive parameters (age, BW, BMI, BSA) retained robust performance, with an RMSE of 36.0 mL (R2=0.70) in external validation. Furthermore, both models outperformed all existing formulas in cross-validation analyses. The models were deployed as open-access calculators at https://mlssv.vip.cpolar.cn (ML_SSV) and https://mlssva.vip.cpolar.cn (ML_SSVa), enabling real-time estimation with SHAP-based interpretability.
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.
