Machine learning-based prediction of in-hospital mortality in patients with decompensated heart failure using the Charlson Comorbidity Index

Figures

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  Figure 1.. Receiver operating characteristic (ROC) curves of Logistic Regression, Random Forest, and Gradient Boosting models for predicting in-hospital mortality using age, sex, hypertension, diabetes mellitus, myocardial infarction, peripheral artery disease, and (if available) Charlson Comorbidity Index (CCI).

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  Figure 2.. Precision–recall (PR) curves of the same models constructed using age, sex, hypertension, diabetes mellitus, myocardial infarction, peripheral artery disease, and (if available) CCI. CCI: Charlson Comorbidity Index.

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  Figure 3.. Model performance comparison across ROC-AUC, PR-AUC, and F1 metrics. The bar chart illustrates the comparative performance of four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Multilayer Perceptron (MLP)—in predicting in-hospital mortality among patients with decompensated heart failure. Gradient Boosting demonstrated the highest ROC-AUC (0.87), PR-AUC (0.76), and F1 score (0.73), indicating the best overall predictive accuracy and calibration among all models. ROC-AUC: receiver operating characteristic area under the curve; PR-AUC: precision–recall area under the curve.

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  Figure 4.. Forest plot demonstrating the discriminative performance of four machine learning models—Gradient Boosting, Random Forest, Multilayer Perceptron (MLP), and Logistic Regression—for predicting in-hospital mortality, based on the area under the receiver operating characteristic curve (AUC). AUC estimates and 95% confidence intervals were obtained using non-parametric bootstrapping with 10,000 resamples and are displayed on a logit scale. Gradient Boosting showed the highest discriminative ability, followed by Random Forest and MLP, whereas Logistic Regression demonstrated the lowest predictive performance.