Key result
A double-layer stacking ensemble model predicted in-hospital mortality among NSTEMI patients with an AUC of 0.987, outperforming individual machine learning models.
Why the study?
AMI remains a leading cause of hospitalization and death in China, and accurate in-hospital mortality prediction is crucial for clinical decision-making in NSTEMI patients.
Does a double-layer stacking ensemble model improve the prediction of in-hospital mortality in NSTEMI patients compared to single machine learning models?
Observational (n=3,061)
No
Does a double-layer stacking ensemble model improve the prediction of in-hospital mortality in NSTEMI patients compared to single machine learning models?
Effect estimate: AUC 0.987
A double-layer stacking ensemble model demonstrated superior performance in predicting in-hospital mortality for NSTEMI patients compared to individual machine learning models.
Should not yet alter NSTEMI risk stratification; leaves open external validation versus GRACE in broader cohorts.
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI) patients. METHODS: In this study, a total of 3061 patients between January 1, 2017 and December 31, 2022 diagnosed with NSTEMI were enrolled in this study. A new method based on Stacking ensemble model is proposed to predict the in-hospital mortality risk of NSTEMI using clinical data. This method mainly consists of three parts. Firstly, oversampling technique was used to alleviate the class imbalance problem. Secondly, the feature selection method of Recursive Feature Elimination (RFE) was selected for effective feature selection. Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer. RESULTS: Patient were divided into the surviving group and the death group, and a total of 57 clinical features showed statistically significant for the two groups and finally included in the subsequent model. The results show that the Area Under Curve (AUC) of the Stacking model proposed in this paper is 0.987, which is higher than that of LR (0.934), DT (0.946), SVM (0.942), RF (0.948), ADB (0.949), ET (0.938) and GBDT (0.920). At the same time, the proposed Stacking model has higher performance than each single model in terms of Accuracy, Precision, Recall and F1 evaluation indicators. CONCLUSIONS: The Stacking model proposed in this paper can integrate the advantages of LR, DT, SVM, RF, ADB, ET and GBDT models to achieve better prediction performance. This model can provide valuable insights for physicians to identify high-risk patients more precisely and timely, thereby maximizing the potential for early clinical interventions to reduce the mortality rate.
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Wang et al. (2024) conducted an observational in Non-ST-segment elevation myocardial infarction (NSTEMI) (n=3,061). Stacking ensemble model vs. Single machine learning models (LR, DT, SVM, RF, ADB, ET, GBDT) was evaluated on In-hospital mortality prediction (AUC 0.987). A double-layer stacking ensemble model predicted in-hospital mortality among NSTEMI patients with an AUC of 0.987, outperforming individual machine learning models.
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