Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance

Figures

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  Fig. 1.. Flowchart of AMI patient inclusion and exclusion, and the ML model development process. AMI, acute myocardial infarction; PCI, percutaneous coronary intervention; ML, machine learning; SMOTE, synthetic minority over-sampling technique; GSCV, grid search with cross-validation; SHAP, shapley additive explanation; AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; RF, Random Forest; LightGBM, Light Gradient Boosting Machine; AdaBoost, Adaptive Boosting; LR, Logistic Regression; KNN, K-Nearest, Neighbors; DNN, Deep Neural Networks.

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  Fig. 2.. Model performance under four processing strategies for predicting IHM in AMI patients post-PCI. (A) AUROC and (B) AUPRC values across different processing levels. IHM, in-hospital mortality.

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  Fig. 3.. Performance comparison of six ML models for predicting IHM in AMI post-PCI. (A) AUROC and (B) AUPRC for models developed with different algorithms. RF, random forest; LightGBM, light gradient boosting machine; AdaBoost, adaptive boosting; LR, logistic regression; KNN, k-nearest neighbor; DNN, deep neural network.

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  Fig. 4.. Performance of three prospective models in predicting IHM post-PCI in AMI patients. (A) AUROC values of prospective models. (B) AUPRC values of prospective models.

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  Fig. 5.. Performance comparison of three prospective models using DeLong and bootstrap tests. (A) DeLong test comparing AUROC values across three prospective models. (B) Bootstrap test comparing AUPRC values across three prospective models.

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  Fig. 6.. Violin plots comparing performance metrics across three predictive models. (A) AUROC distribution of three prospective models. (B) AUPRC distribution of three prospective models.

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  Fig. 7.. Case distribution in the training cohort before and after SMOTE augmentation. (A) Original distribution. (B) Post-SMOTE distribution.

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  Fig. 8.. SHAP summary plot for the LightGBM model predicting IHM in AMI patients post-PCI. The plot shows each feature’s contribution to the model’s predictions.

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  Fig. 9.. Feature importance in the LightGBM model predicting IHM in AMI patients post-PCI. (A) Radar plot of feature importance. (B) SHAP bar plot showing individual feature contributions.

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  Fig. 10.. Clinical application of the LightGBM model for IHM prediction in AMI patients post-PCI. The application uses 32 input features to predict IHM probability (e.g., 37.1%). The accompanying SHAP force plot visualizes individual feature contributions: blue bars (right) push the prediction toward mortality, while red bars (left) favor survival.