Wang Li, Zhang Yu, Li Feng, Li Caiyun, Xu Hongzeng
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China.
PLoS One. 2024 Dec 31;19(12):e0312448. doi: 10.1371/journal.pone.0312448. eCollection 2024.
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.
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.
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.
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.
急性心肌梗死（AMI）仍是中国住院和死亡的主要原因。准确预测住院患者的死亡率对于非ST段抬高型心肌梗死（NSTEMI）患者的临床决策至关重要。
本研究纳入了2017年1月1日至2022年12月31日期间共3061例诊断为NSTEMI的患者。提出了一种基于Stacking集成模型的新方法，利用临床数据预测NSTEMI患者的住院死亡风险。该方法主要由三部分组成。首先，采用过采样技术缓解类别不平衡问题。其次，选择递归特征消除（RFE）的特征选择方法进行有效特征选择。最后，设计了独特的双层Stacking模型以提高算法性能。选择逻辑回归（LR）、决策树（DT）、支持向量机（SVM）、随机森林（RF）、自适应提升（ADB）、极端随机树（ET）和梯度提升决策树（GBDT）这七种经典人工智能方法作为模型第一层基础模型的候选模型，并选择极端梯度增强（XGBOOST）作为第二层的元模型。
将患者分为存活组和死亡组，共有57个临床特征在两组间显示出统计学显著性，最终纳入后续模型。结果表明，本文提出的Stacking模型的曲线下面积（AUC）为0.987，高于LR（0.934）、DT（0.946）、SVM（0.942）、RF（0.948）、ADB（0.949）、ET（0.938）和GBDT（0.920）。同时，在准确率、精确率、召回率和F1评估指标方面，所提出的Stacking模型比每个单一模型具有更高的性能。
本文提出的Stacking模型可以整合LR、DT、SVM、RF、ADB、ET和GBDT模型的优势，以实现更好的预测性能。该模型可为医生更准确、及时地识别高危患者提供有价值的见解，从而最大限度地发挥早期临床干预的潜力，降低死亡率。
