Key result
Soft voting ensemble model predicts in-hospital mortality in ICU HF patients with 0.86 AUC.
Why the study?
Assessing in-hospital mortality risk among heart failure patients in the ICU is crucial for clinical decision-making, but comprehensive models accurately predicting their prognosis are lacking.
Can machine learning-based models accurately predict in-hospital mortality in ICU-admitted heart failure patients?
Observational
Can machine learning-based models accurately predict in-hospital mortality in ICU-admitted heart failure patients?
Effect estimate: AUC 0.86
An ensemble machine learning model based on a soft voting mechanism can accurately predict in-hospital mortality risk among heart failure patients in the ICU (AUC 0.86).
May support ensemble ML for HF ICU mortality prediction; leaves open prospective validation before clinical adoption.
Background: Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data. Methods: This study, based on the MIMIC-III database, extracted demographic characteristics, vital signs, laboratory test values, and comorbidity information of heart failure patients using structured query language. LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. An ensemble learning model based on a soft voting mechanism was constructed. Model performance was evaluated using accuracy, recall, precision, F1 score, and AUC values through cross-validation and on an independent test set. Results: In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. Additionally, RF and GB models also performed well, with RF achieving an accuracy of 0.79 and an AUC of 0.79 on the independent test set, while the GB model achieved an accuracy of 0.77 and an AUC of 0.79. In contrast, other models such as LR, SVM, and KNN exhibited poorer performance in terms of accuracy and AUC values, indicating the significant advantage of ensemble methods in handling complex clinical prediction tasks. Conclusion: This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. The overall performance of the ensemble learning model confirms its effectiveness as an adjunct clinical decision-making tool. Future research should further optimize the models and validate them in a broader patient population to enhance their practical utility and accuracy in real clinical settings.
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Su et al. (2025) conducted an observational in Heart failure. Soft voting ensemble learning model vs. Other machine learning models (LR, RF, GB, SVM, KNN) was evaluated on In-hospital mortality prediction (AUC 0.86). A soft voting ensemble learning model effectively predicted in-hospital mortality among ICU-admitted heart failure patients, achieving an accuracy and AUC of 0.86.
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