## 4 Discussion
SHAP demonstrated that several key variables had a substantial impact on the predictions of the RF model. APSIII, LDH, lactate, AF count, ACEI, AST, creatinine, PT, RDW, CCI, history of congestive heart failure, and LOS were identified as the top 12 most important predictors of in-hospital mortality. APSIII, which quantifies the severity of acute illness based on physiological parameters, was identified as the most important predictor. Higher APSIII scores demonstrated a greater risk of in-hospital mortality, reflecting poorer physiological stability at admission. Elevated LDH levels were found to strongly contribute to the model's prediction of mortality. Increased LDH levels indicate tissue damage and a worse prognosis, which is consistent with the existing literature linking elevated LDH to adverse outcomes in critical illnesses [37; 38; 39]. High lactate levels are indicative of poor tissue perfusion and oxygenation, which is a known predictor of mortality in NSTEMI patients [40; 41; 42]. The SHAP analysis confirmed that higher lactate levels significantly increased the risk of death, highlighting the importance of early lactate measurement in the management of these patients. The frequency of atrial fibrillation episodes was another critical factor. The presence of AF, especially when recurrent or sustained, significantly contributed to a higher risk of in-hospital mortality. The use of ACEIs was found to be inversely related to mortality. The SHAP analysis suggested that ACEI-treated cohorts demonstrated reduced odds of in-hospital mortality. This is consistent with clinical guidelines that recommend ACEIs for heart failure and NSTEMI management to improve outcomes [43; 44; 45]. Elevated AST levels were associated with liver dysfunction and a potentially worse overall health status. In the context of NSTEMI, high AST levels can reflect liver injury or ischemia, thus contributing to higher mortality risk. Elevated serum creatinine levels, indicating impaired renal function, showed an association with heightened mortality risk. Renal dysfunction is a well-established risk factor for poor outcomes in NSTEMI patients, often reflecting multi-organ failure [46]. PT is a marker of coagulation status. Elevated PT indicates heightened risks of both hemorrhagic and thrombotic complications, which are crucial considerations in the care of NSTEMI patients, particularly under anticoagulant therapy. High RDW values increased the risk of death. Increased RDW often reflects systemic inflammation or chronic disease, which may contribute to adverse cardiovascular outcomes [47; 48; 49]. A higher CCI, which accounts for the burden of comorbidities, was linked to a greater likelihood of mortality during hospitalization, which suggests that patients with multiple pre-existing health conditions are at a greater risk of poor outcomes following NSTEMI. A previous history of congestive heart failure was a key risk factor for mortality, as heart failure can exacerbate the stress on the heart during an NSTEMI event. Longer hospitalization was strongly associated with an increased risk of mortality. Extended stays often reflect complications or the severity of illness, suggesting that patients with prolonged hospitalizations require more intensive care and have worse prognoses. Additionally, the SHAP force plot dissected the attribution of individual features to the model's decision-making for a specific patient, visualizing how algorithmic reasoning was informed by these variables. The application of SHAP allowed for the quantification of each feature's contribution to the predicted outcome, thereby enhancing the transparency of the RF model. The results suggest that clinicians should assess APSIII scores upon patient admission. When LDH, lactate, and AST levels are elevated, timely evaluation is recommended. Additionally, increased attention should be given to patients with recurrent atrial fibrillation or a history of heart disease.