Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure

Figures

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  Figure 1. Flowchart of the participants included in the study.

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  Figure 2. Feature selection based on Boruta algorithm. Green represents acceptable variables.

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  Figure 3. The association between variables and hospital mortality. Creatinine (A), PCT (B), pH (C), LDH (D), PLT (E), NT-proBNP (F), WBC (G): the restricted cubic splines with four knots. The horizontal dashed line represents the reference OR of 1.0.

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  Figure 4. Establishment and validation of the machine learning prediction model. (A,B) Present the ROC curves. (C,D) Show the calibration curves. (E,F) Display the decision curve analysis. (G,H) Illustrate the recall-precision curves.

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  Figure 5. Performance metrics comparison across Six machine learning models. (A,B) Represent the training set and the validation set, respectively. The evaluated metrics include F1 score, recall, precision, negative predictive value (Neg Pred value), positive predictive value (Pos Pred value), specificity, and sensitivity.

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  Figure 6. The shapley additive exPlanations values of the best prediction model, XGB. (A) Average impact of features on model predictions. (B) Detailed impact analysis of each feature SHAP interpretation of the XGB model. Every dot in a row symbolizes a patient, and its color denotes the feature value—yellow denotes a value that is greater and purple denotes a value that is lower. The more dispersed the points of the graph represent the greater the impact of the variables on the model. (C,D) Personalized predictions for a patient. The risk and protective variables are symbolized by the yellow and plum bars. Higher functional significance is indicated by longer bars.

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  Figure 7. External validation shows excellent performance in models. (A) XGB validation set ROC curve. (B) XGB validation set calibration curve. (C) XGB validation set decision curve analysis curve. (D) Average impact of features on model predictions.