Systematic assessment of mixed imputation methods and explainable machine learning

Table 1 Alignment of the study with transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence (2024) reporting domains and key statistical checks

Evaluation domain	TRIPOD + AI item	Study implementation	Critical commentary
Data integrity	Item 11: Missing data handling	Strong: Used HDI-MF-Gower hybrid imputation; adaptive initialization	The hybrid design is intended to improve initialization for iterative imputation; however, missingness assumptions and MNAR-oriented sensitivity analyses are not reported
Validation rigor	Item 10/22: Validation strategy	Weak: Random split (7:3) within single-center cohort (type 2a)	A random split within a single-center cohort may inflate performance estimates; temporal and/or geographical external validation is not shown
Discrimination	Item 13a: Model performance	Moderate: AUC = 0.853 (extra trees)	Statistically superior to KNN/support vector machine (P < 0.05), but not significant vs XGBoost (P = 0.098) or RF (P = 0.196). Gain may be data-driven rather than model-driven
Calibration	Item 13b: Calibration plot	Gap: Not explicitly detailed in primary comparison	Essential for clinical utility. High AUC does not guarantee accurate risk probability estimation
Interpretability	Item 22: Model explanation	Strong: SHAP summary and dependence plots used	Identified clinically relevant markers (albumin, lymphovascular invasion), providing surface validity. Needs stability testing across folds
Reproducibility	Item 25: Availability of code/model	Gap: Online tool mentioned but access/archiving unclear	Full adherence requires public repository (e.g., GitHub) and clear version control for clinical deployment Availability is not clearly documented (no persistent link, repository, or archived version); providing code/model cards, versioning, and a persistent access route would strengthen reproducibility

TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; AI: Artificial intelligence; MNAR: Missing not at random; AUC: Area under the curve; KNN: K-nearest neighbors; RF: Random forest; SHAP: Shaply Additive exPlanation.