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

Objective: This study compared the performance of different machine learning models in predicting in-hospital mortality in patients hospitalized for heart failure. Methods: Demographic and clinical data of 229 patients were retrospectively reviewed for this study. The variables included age, sex, hypertension, diabetes, coronary artery disease, peripheral artery disease, chronic kidney failure, Charlson comorbidity index, and length of hospital stay. In-hospital mortality ("in-hospital death") was defined as the dependent variable. The machine learning methods used included logistic regression, random forest, gradient boosting, and multilayer perceptron models. The model performance was evaluated using the receiver operating characteristic area under the curve, precision-recall area under the curve, F1 score, and Brier score. Results: In-hospital mortality was observed in 7 of the 229 patients included in the study (3.1%). The mean age and frequency of chronic kidney disease were higher among deceased patients, whereas the Charlson comorbidity index did not differ significantly between survivors and non-survivors. In model comparisons, the Gradient Boosting model showed the best overall performance (ROC-AUC: 0.87, PR-AUC: 0.76, F1: 0.73; Brier score: 0.12). Conclusion: The Gradient Boosting model demonstrated the highest performance in predicting in-hospital mortality in patients with heart failure. Machine learning algorithms offer strong potential beyond traditional statistical approaches for the prognostic prediction of complex clinical scenarios such as heart failure.