Explainable machine learning for hypertension prevalence classification: a cross-sectional study in Hainan Province, China

Figures

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  Figure 1. Flow chart of this study. AUC, the area under curve; DCA, decision curve analysis; SHAP, SHapley additive explanation; LIME, Local interpretable model-agnostic explanations.

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  Figure 2. Feature selection was performed in the training set using the random forest algorithm feature importance score. BMI, body mass index; DM, diabetes mellitus; EL, educational level; FHH, family history of hypertension; IL, income level; TG, triglyceride; UALB, urinary albumin.

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  Figure 3. Correlation of the 10 key features. These features were independent of each other and there was no significant covariance. EL, educational level; IL, income level; BMI, body mass index; DM, diabetes mellitus; FHH, family history of hypertension; TG, triglyceride; UALB, urinary albumin.

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  Figure 4. AUC of six machine learning models in the validation set. AUC, the area under curve; ROC, receiving operating characteristic curve. AdaBoost, adaptive boosting; XGBoost, eXtreme gradient boosting.

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  Figure 5. Calibration curve of six machine learning models. AdaBoost, adaptive boosting; XGBoost, eXtreme gradient boosting.

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  Figure 6. Decision curve analysis of six machine learning models. AdaBoost, adaptive boosting; XGBoost, eXtreme gradient boosting.

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  Figure 7. SHAP summary plot of all features that contribute to the XGBoost model. (A) Importance ranking of features represented by SHAP. The bar plot shows the importance of each feature in the model prediction output. (B) The distribution of the effect of each feature on the model output. Each row represents a feature, and in each row, each dot represents a participant. The color of the dots represents the feature values: red dots represent higher feature values and blue dots represent lower feature values. EL, educational level; IL, income level; BMI, body mass index; DM, diabetes mellitus; FHH, family history of hypertension; TG, triglyceride; UALB, urinary albumin.

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  Figure 8. SHAP force analysis and LIME algorithm used to explain individualized predictions. (A,B) Were a sample with hypertension explained using SHAP force analysis and LIME algorithm respectively. (C,D) Were a sample without hypertension explained using SHAP force analysis and LIME algorithm respectively. In (A) and (C), the red and blue bars represent risk and protective factors, respectively, and the longer bars indicate greater feature importance. In (B) and (D), the left part displays the classification probability results. The middle part of the figure showed the ranking of all features in terms of the magnitude of their influence on the prediction results, while the length of each feature bar indicated the importance of that feature in the prediction, with longer lengths representing greater importance. The right part lists the specific values of all included features. SHAP, SHapley additive explanation; LIME, local interpretable model-agnostic explanations.

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  Figure 9. SHAP dependence plot of the interaction between sex and important features. (A) The interaction of age and sex. (B) The interaction of smoking and sex. (C) The interaction of education level and sex. (D) The interaction of BMI and sex. The X-axis represents the value of the variable labeled on the horizontal axis, and the left Y-axis indicates the corresponding SHAP value, reflecting the feature's contribution to model classification outcomes. The color of each point reflected the feature value of the right Y-axis heading. When the x-coordinate value of the sample point was larger, the variable of the x-axis was larger. When the value of the left Y-axis coordinate of the sample point was larger, the risk of disease of the sample point was larger. When the color of the sample point was more red, the higher the value of the right Y-axis indicator. SHAP, SHapley additive explanation; BMI, body mass index.

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  Figure 10. Web-based screening tool for hypertension prevalence identification in the general population.