Explainable Machine Learning for Risk Prediction of Reduced Quality of Life in Hypertension

Figures

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  Figure 1. Cross-validated performance of machine learning classifiers for physical and psychological QoL classification. ROC curves generated from pooled out-of-fold predicted probabilities during stratified 10-fold cross-validation for physical (a) and psychological QoL (b). AUC values for physical (c) and psychological QoL (d). Display pooled sensitivity and specificity estimates across cross-validation folds for physical (e) and psychological QoL (f).

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  Figure 2. Predicted probability distributions for reduced and non-reduced quality of life (QoL) across machine learning classifiers. (a–f) show probability distributions for physical QoL, and (g–l) show distributions for psychological QoL. Blue bars represent non-low QoL, and Orange bars represent low QoL. Greater separation between distributions indicates improved discrimination between outcome classes.

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  Figure 3. Model ranking stability across stratified 10-fold cross-validation for physical and psychological QoL classification. (a and c) show the mean rank and standard deviation of each classifier across the 10 cross-validation folds for physical and psychological QoL, respectively. A lower rank indicates better relative model performance. Smaller standard deviation indicates more stable performance across folds. (b and d) show the distribution of model ranks across folds using boxplots, where narrower boxes indicate more consistent model ranking.

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  Figure 4. Feature importance and SHAP interpretation of predictors of reduced QoL. (a and b) show Random Forest feature importance for reduced physical and psychological QoL, respectively, where higher values indicate greater contribution to model prediction. (c and d) show SHAP summary plots from the Gradient Boosting models for reduced physical and psychological QoL, respectively. In the SHAP plots, each point represents one participant. The horizontal position shows whether a feature increased or decreased the predicted risk of reduced QoL. Positive SHAP values indicate higher predicted risk of reduced QoL, while negative SHAP values indicate lower predicted risk. The color scale represents the feature value, with higher values shown in red and lower values shown in blue.