## 1 Introduction  
Acute coronary syndrome (ACS) has become the prime cause of mortality and morbidity globally [1], with mortality rates persistently rising in recent years [2, 3]. ACS is broadly classified into three main types: ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina (UA) [4]. Recent studies have indicated that in contrast to STEMI, which has a decreasing incidence, the incidence of NSTEMI has been rising [5, 6]. As a result, NSTEMI has become the main type of ACS in recent years [7]. It is related to higher rates of mortality, reinfarction, and stroke, as well as a worse unadjusted long-term prognosis, reflecting its significant clinical burden [8]. Patients with NSTEMI are more likely to be older and have more comorbidities compared to those with other forms of ACS [9]. The growing burden emphasizes the urgent need for more precise and adaptable predictive tools to improve risk stratification and patient outcomes. However, research on mortality in NSTEMI patients remains relatively limited compared to other cardiovascular diseases. Although studies have shown that early intervention in NSTEMI patients can significantly reduce the risk of recurrence and shorten hospitalization, highlighting the importance of timely management, in-hospital mortality is not negligible [10]. In-depth exploration of independent risk factors for in-hospital mortality can not only optimize risk stratification tools but also guide individualized treatment [11,12]. Therefore, the identification of controllable predictors of in-hospital mortality has dual significance for the realization of precision medicine and the rational allocation of medical resources. The Thromolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) scores have been widely used to predict adverse outcomes in NSTEMI patients [13-19]. While these tools provide valuable guidance for patient stratification and management, they have significant limitations. These scores typically rely on linear relationships, failing to capture complex, non-linear interactions among variables. Advances in machine learning (ML) have empowered the development of robust predictive tools for estimating mortality risk in patients with NSTEMI. ML models can leverage large datasets to detect complex patterns and higher-order interactions among predictive features, leading to significant improvements in prediction accuracy compared to traditional scoring systems [20]. Studies have shown that ML models outperform traditional risk scoring systems [21]. [PERSON] et al. obtained data from the national cardiovascular disease database for Malaysia, with 39 features and four ML algorithms: Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Naive Bayes (NB), and performed a comparison with the TIMI score [22]. The predictive performance, evaluated using the Area Under the Curve (AUC), ranged from 0.73 to 0.89, with all AUCs exceeding that of the TIMI score (0.55). [PERSON] et al. used deep learning, RF, logistic regression, GRACE, and TIMI to estimate in-hospital mortality for patients with NSTEML. The results suggested that ML models were better than traditional risk scoring systems [12]. [PERSON] et al. employed RF, XGBoost, TIMI, and GRACE to forecast in-hospital mortality for NSTEMI patients [23]. The AUCs were 0.889, 0.888, 0.669, and 0.872, respectively. As demonstrated by these results, ML algorithms surpass traditional risk scoring models. Despite their impressive performance, the 'black-box' nature of ML models has hindered their clinical adoption [24,25]. In response to this, Shapley additive explanations (SHAP) was developed to enhance transparency by enabling the identification of critical predictive factors and quantifying the contribution of each feature [26]. However, few studies have utilized SHAP to explain ML models, resulting in a lack of transparency in findings.