## 1 Introduction
Moreover, key predictors can be found by using more advanced feature selection methods, such as wrapper and embedded, in order to further increase ML model interpretability and robustness [20; 21]. However, the main weakness of manual FE is that it is prone to designer's assumptions bias, which can result in overlooking importantinformation in the data. Furthermore, manual methods heavily rely on domain expertise, which may not always be capable of identifying complex, nonlinear relationships that automated feature learning approaches can uncover [22; 23]. This motivates the need for systematic, data-driven approaches that are complementary or, in some cases, replace FE. To address the above issues, a groundbreaking method called XAI has emerged recently. By offering a more objective assessment of feature relevance than human techniques, XAI provides automated and transparent explanations of feature contributions to the model's predictions [24]. Furthermore, XAI approaches differ from conventional FE as they reveal latent data linkages, addressing interpretability challenges in complex ML models--an issue particularly critical in industries like healthcare, where patient safety is paramount. One of the most popular XAI approaches, SHAP, utilizes Shapley values, which are based on cooperative game theory, to quantify each feature's contribution to the model predictions. For the purpose of heart attack prediction, SHAP details the ways in which hypertension and cholesterol influence model behavior on a global scale as well as patient-specific results. Modern black-box models can benefit from SHAP, a state-of-the-art technique for enhancing transparency [25; 26], as it provides reliable and consistent explanations. Another XAI technique that is worth noting is LIME, which develops interpretable diverse models to approximate the behavior of complex models locally for specific predictions [27]. For the purpose of heart attack prediction, LIME provides clinicians with a means to understand why a particular patient was flagged as high risk by the model by highlighting the most important features contributing to that prediction. Due to this interpretability, healthcare professionals can make better decisions on diagnosis and treatment [28; 29]. Although the study does not rely on traditional manual FE techniques, domain knowledge remains integral to interpreting the results. XAI tools are utilized to identify the most influential features, but their clinical validation and interpretation require healthcare expertise. This approach aims to reduce the manual overhead of FE while preserving the critical role of domain-informed analysis in high-stakes applications like heart attack prediction. Previous research has examined pipelines that integrate SMOTE, grid search, and shallow ANN architectures; however, many have concentrated predominantly on attaining high predictive metrics, neglecting the essential requirements for clinical interpretability and simplicity, particularly in small medical datasets. This study seeks to address this gap by concentrating on low-complexity, interpretable ANN models that can be meaningfully explained using SHAP and LIME, so ensuring the models are both precise and clinically applicable. Unlike black-box deep learning approaches, which often lack transparency, our approach prioritizes models that balance performance with explainability, a combination that is essential for adoption in real-world cardiology settings where model decisions must align with domain knowledge and be trusted by clinicians. The study uses a publicly available UCI cardiac dataset [30] and applied SMOTE to fix class imbalance. The dataset gathers various heart analysis parameters of patients using a variety of medical sensors, including ECG sensors, blood pressure monitors, and biochemical analyzers. Essential physiological data from these sensors allows for precise analysis and the prediction of cardiac disease. The data collected from these diverse sensors provide a comprehensive overview of patient's cardiac health conditions.