## 3 Results
### Interpretability Analysis  
The summary plot (Figure 3) demonstrates the RF model with all features combined. Only the top 20% of the features are displayed. The x-axis represents the SHAP values, which indicate how each feature value affects the model output. The y-axis is ordered by each feature's importance. Features that exert the greatest influence on the model's predictions occupy positions towards the top of the axis. These include metrics like APSIII, LDH, lactate, and others. Each point stands for a row in the initial dataset. The color of the dots represents the value of the feature. The chromatic scale of the data points, with red signifying greater feature intensity and blue indicating lesser feature intensity, reflects  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Model** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{2}{*}{Our study} & \multirow{2}{*}{RF} & \multicolumn{2}{c}{Medical Information Mart for \(\quad\)} & \multirow{2}{*}{0.8513} & \multirow{2}{*}{0.9016} & \multirow{2}{*}{0.8513} & \multirow{2}{*}{0.8670} & \multirow{2}{*}{0.8903} \\  & & \multicolumn{2}{c}{Intensive Care} & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [7]} & Logistic & Affiliated Hospital of North Sichuan & \multirow{2}{*}{0.756} & \multirow{2}{*}{-} & \multirow{2}{*}{0.625} & \multirow{2}{*}{-} & \multirow{2}{*}{0.749} \\  & regression (LR) & & Medical College & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [23]} & \multirow{2}{*}{RF} & \multicolumn{2}{c}{Korean Registry of Acute Myocardial \(\quad\)} & \multirow{2}{*}{0.792} & \multirow{2}{*}{-} & \multirow{2}{*}{0.758} & \multirow{2}{*}{0.220} & \multirow{2}{*}{0.889} \\  & & \multicolumn{2}{c}{Indarction for Regional} & & & & & \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{Cardiocerebrovascular Centers} & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [22]} & \multirow{2}{*}{XGBoost} & Malaysian National Cardiovascular & \multirow{2}{*}{0.874} & \multirow{2}{*}{-} & \multirow{2}{*}{0.716} & \multirow{2}{*}{-} & \multirow{2}{*}{0.883} \\  & & Disease Database & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the performance of in-hospital mortality prediction models for NSTEMI patients in existing studies.  
Figure 2: Receiver operating characteristic (ROC) curve comparison for various predictive models based on different sets of features. (**a**) ROC curve based on Lasso feature selection. (**b**) ROC curve based on RF feature selection. (**c**) ROC curve for models incorporating all features.  
the relative impact of each attribute. For binary classification variables, the scale contains only two colors. As shown in Figure 3, the use of ACEIs can protect patients, while higher values of other variables represent risk factors.  
Figure 4 displays a SHAP force plot, which provides an explanation of how the RF model makes a prediction for a single observation. As illustrated in Figure 4, the predicted probability for the sample is 0.80. A higher score indicates a tendency towards death, while a lower score indicates a tendency towards survival. Features highlighted in red indicate a positive influence on the model's predicted value, effectively boosting the probability. Conversely, features colored in blue signify a negative impact on the predicted value, thus decreasing the probability of prediction. The magnitude of the impact is intuitively represented by the length of the corresponding bar. As can be seen, AF count, lactate, APSIII, AST, and LDH have a stronger association with a poorer outcome. The absence of a history of congestive heart failure is considered a protective factor for the patient.  
Figure 3: SHAP summary plot for Random Forest model based on all features. Note: Lactate dehydrogenase (LDH); Aspartate aminotransferase (AST); Prothrombin time (PT); Red cell distribution width (RDW); Charlson comorbidity index (CCI).