## 2 Literature Review
### XAI in Healthcare  
The \"black box\" nature of ML models often limits their adoption in clinical settings. The accuracy of these models may be high; however, their lack of transparency makes it challenging to understand the decision-making process for specific scenarios. The goal of XAI is to overcome this difficulty by providing a rationale for the model's predictions [56; 57; 58]. SHAP and LIME emerge as two popular methods for explaining individual predictions and feature contribution [59; 60]. [PERSON] et al. [61] used the SHAP technique to analyze deep ML models developed for predicting in-hospital mortality in patients with initial AMI. SHAP helps increase model transparency, which is directly related to improving patient outcomes by helping doctors understand the most important risk variables. Through the use of SHAP analysis, it was revealed that key predictors of mortality risk include D-dimer, brain natriuretictic peptide, and cardiogenic shock. [PERSON] et al. [62] used ML models to determine the effect of dietary antioxidants on disease prediction, showing the importance of feature selection for clinical decision-making and its ability to understand the results. The integration of key antioxidant, demographic, and health-related information improved model performance. SHAP analysis of feature significance shows that naringenin, magnesium, and vitamin C are the most important contributors. [PERSON] et al. [63] used SHAP analysis to identify important clinical features that contribute to the mortality prediction of HF patients, thereby increasing model transparency and interpretability. This study was a retrospective cohort study that used Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and eICU Database. The goal was to employ interpretable ML to predict 28-day all-in-hospital mortality causes in patients with HF and highlighted age, mechanical ventilation, and anion gap as the key factors. [PERSON] et al. [60] used LIME and SHAP techniques to enhance ML model interpretability and identify key prognostic factors for Nasopharyngeal Carcinoma (NPC) survival, such as age, T-stage, and M-stage. SHAP measured the influence of each feature on predictions, while LIME offered case-specific explanations that allowed for individualized risk assessment and well-informed clinical decision-making. Similarly, [PERSON] et al. [64] used LIME and SHAP to increase the interpretability of the ML model. SHAP quantified the influence of each feature on predictions and identified key risk factors, whereas LIME provided case-specific justifications. Existing heart attack prediction models often overlook the imbalance in the UCI dataset, where negative instances are significantly fewer than positive ones. This imbalance leads to biased predictions and misleading evaluations, especially when accuracy is the primary metric. Unlike prior studies that focus mainly on accuracy, our research incorporates precision, recall, and F1-score for a more balanced assessment. However, a more critical limitation in the previous literature is the lack of interpretability in AI-driven predictions, as most ML models function as black boxes, making it challenging for healthcare professionals to trust their outputs. To address this, XAI techniques, specifically SHAP and LIME, are integrated to enhance transparency and feature identification. Unlike conventional approaches that rely on manual FE, which is resource-intensive and domain-dependent, our study eliminates the need for such manual efforts by using XAIto automatically highlight the most relevant features contributing to predictions. This ensures that medical practitioners can make informed decisions based on interpretable model insights rather than relying solely on opaque algorithmic outputs. Furthermore, our research uniquely combines XAI with a high-performing ANN model, achieving both accuracy and interpretability.