Table 1.
Baseline Characteristics of Training and Testing Cohorts.
Fig 1.
Optimization Performance Comparison of Metaheuristic Algorithms.
Note: Box plots illustrating optimization stability and robustness across CEC2022 test functions over 30 independent runs.
Fig 2.
Convergence Behavior Comparison.
Note: Convergence trajectories demonstrating search efficiency and local optima avoidance capabilities.
Table 2.
Cross-validation performance metrics on the training set.
Fig 3.
Training Set Performance Evaluation.
Note: (A) ROC curve; (B) Precision-Recall curve.
Table 3.
Internal validation performance metrics.
Fig 4.
Testing Set Classification Performance.
Note: (A) ROC curve; (B) Precision-Recall curve.
Fig 5.
Machine learning interpretability visualization.
Note: (A) SHAP summary plot; (B) SHAP feature importance ranking; (C) Heat map of SHAP interaction.
Fig 6.
Decision curve analysis for predictive models.
Note: (A) Training set; (B) Testing set. Net benefit (Y-axis) calculated against two extreme scenarios: “treat all” (red dashed) and “treat none” (black dashed).
Fig 7.
Clinical decision support system interface.
Note: The interface integrates feature entry (“Input Parameters”), predictive computation (“Calculate”), and risk output (“Prediction Results”) modules.