Interpretable SVM Model for Predicting CMV Infection in Seropositive Kidney Transplant Recipients: A Single-Center Retrospective Study

Figure 1. Flow chart of the study. A total of 243 kidney transplant recipients were screened (June 2023–December 2024). After applying the exclusion criteria, 162 recipients were included and split into a training set (n=115) and a testing set (n=47).

Figure 2. Feature selection based on the Boruta algorithm. The horizontal axis is the name of each variable, and the vertical axis is the Z value of each variable. The box plot shows the Z value of each variable during model calculation. The green boxes represent important variables, yellow boxes represent tentative variables, and red boxes represent unimportant variables. Blue boxes represent shadow features variables, which serve as reference importance.

Figure 3. Receiver operating characteristic (ROC) curves of the six prediction models. (A) Training cohort. (B) Validation cohort. The area under the ROC curve (AUC) with 95% confidence intervals for each model is shown in the legend.

Figure 4. Calibration curves of the six prediction models. (A) Training cohort. (B) Validation cohort. The dashed diagonal line indicates perfect calibration. Brier scores (95% CI) for each model are shown in the legend.

Figure 5. Decision curve analysis of the six prediction models. (A) Training cohort. (B) Validation cohort. Net benefit is plotted against threshold probability.

Figure 6. SHAP-based interpretation of the final model. (A) Importance ranking plot of features of the logistic regression model. (B) Characteristic attributes in SHAP. The abscissa is the SHAP value, and each line denotes a feature. Higher eigenvalues are indicated by red dots, and lower eigenvalues are indicated by blue dots.

Figure 7. Receiver operating characteristic (ROC) curves of prediction models built using the top three predictors. (A) Training cohort. (B) Validation cohort. The area under the ROC curve (AUC) for each model is shown in the legend.