## 4 Discussion  
ACS continues to contribute to global morbidity and mortality, and NSTEMI has become a significant concern, owing to its rising prevalence and unfavorable outcomes. In this study, we compared four popular ML models and two feature selection methods. Among these, the RF model with all features demonstrated the best performance, with the highest accuracy (0.8561). SHAP for RF demonstrated each feature's importance ranking and provided insights into individual predictions, helping to explain the decision-making process. Selecting features is a crucial step in ML, aimed at reducing the dimensionality of the dataset and improving model performance by retaining only the most informative features. In this study, we employed two widely used feature selection techniques: Lasso and feature importance based on RF. Both methods helped to identify the most relevant variables for predicting in-hospital mortality in NSTEMI patients. The top 20% of features selected by both Lasso and RF-based feature importance included variables such as lactate [32], APSIII, and RDW [33], which were consistently identified as strong predictors of mortality. However, surprisingly, the model that utilized all available features, without any feature selection, yielded the best performance. This finding can be attributed to the complexity and heterogeneity of clinical data. In many medical datasets, the relationships between variables are often non-linear and multifaceted, making it difficult to rely solely on a small subset of selected features. While feature selection methods like Lasso and RF-based importance ranking can help in identifying key predictors, they might also exclude important variables that, when combined, offer complementary or hidden insights into patient outcomes. By including all features, the model is able to capture a broader range of patient characteristics, laboratory measurements, and clinical observations, which together provide a more accurate representation of the risk factors influencing mortality [34]. It is also worth noting that the decision to use all features could have been influenced by the high quality and quantity of data available. In our study, missing data were carefully imputed, and appropriate techniques like SMOTEENN were used to balance class distribution, ensuring that the model could leverage the full spectrum of available information without bias. Thus, the result underscores the importance of considering the unique nature of the dataset and the potential benefits of utilizing all available data in complex clinical prediction tasks. While feature selection methods are valuable tools, in some cases, especially with high-dimensional healthcare data, it may be more advantageous to incorporate all features for optimal model performance. We compared four widely used ensemble learning algorithms: RF, AdaBoost, GBDT, and XGBoost, to predict in-hospital mortality in patients with NSTEMI. Specifically, the RF model outperformed the other algorithms, achieving the highest accuracy (0.8513), precision (0.9016), recall (0.8513), F1 score (0.8670), and AUC (0.8903). Moreover, learning curve analysis demonstrated the superior generalization capability of RF, as evidenced by the convergence of training and test error rates with increasing sample size. This aligns with the existing literature, which has consistently shown that RF performs well  
Figure 4: A SHAP force plot for a single instance, visualizing feature contributions to the predicted outcome. on structured clinical data, due to its ability to deal with complex feature interaction relationships [29; 35; 36]. One of the key innovations in the study is the incorporation of SHAP to enhance the interpretability of the ML. SHAP provides a robust framework for interpreting the contribution of individual features to the model's predictions, both globally and locally. It is particularly important in clinical settings, where transparency is essential for fostering trust and facilitating decision-making.