## 5 Conclusions and Future Work  
The main objective of this study is to predict heart disease occurrence using ML and DL models, emphasizing both predictive accuracy and model interpretability. Heart attack prediction was performed using the UCI dataset, which includes key cardiac health attributes such as heart rate, blood pressure, cholesterol levels, and stress test results. These parameters were collected through various medical sensors, including ECG sensors, blood pressure monitors, and biochemical analyzers. After tackling the class imbalance nature of the dataset, it was fed into various ML models, among which ANN demonstrated the highest classification performance, achieving 96% accuracy, 96.1% recall, and a 95.7% F1-score. By utilizing minimal data preprocessing, this method significantly reduces execution time compared to prior approaches. However, optimizing hyperparameters remains an area for further refinement to enhance predictive accuracy. Additionally, exploring advanced neural network architectures, such as adversarial networks or attention-based models, could potentially improve performance further. The key contribution and novelty of this study is the integration of XAI techniques, specifically SHAP and LIME, to improve the interpretability of the outperforming ANN model. This focus on XAI distinguishes our work, enabling healthcare practitioners to comprehend the model's decision-making process. These techniques provide transparency into the most influential sensor-derived features in heart disease prediction, fostering trust in AI-driven outcomes and supporting clinicians in making informed decisions. Improved interpretability ensures that the model's predictions can be effectively adopted in real  
Figure 13: Shape dependency plots for age and chest pain features. world clinical settings, where understanding the rationale behind each decision is critical for patient care. A key limitation of this study is the relatively small sample size of the dataset. Since the data are derived from an ICU cohort, caution is required when generalizing the findings to broader populations. Additionally, the limited dataset comprising 303 samples may contribute to model variance, even with the application of SMOTE and cross-validation. Additionally, given the small size of the UCI dataset and the synthetic nature of SMOTE-generated samples, future work should include larger, multicenter datasets and real-world clinical validation to ensure the robustness and generalizability of our findings. Beyond heart disease prediction, the methodologies presented in this research can be extended to other critical conditions, including diabetes and cancer. Furthermore, incorporating Internet of Things (IoT) technology into the proposed framework would facilitate real-time monitoring of patients' health metrics through wearable and non-invasive sensors, enabling proactive medical interventions. By combining accurate AI-driven predictions, enhanced interpretability through XAI techniques, and IoT-based healthcare solutions, this work may be adopted to develop a more transparent, efficient, and patient-centric healthcare system, bridging the gap between cutting-edge AI advancements and practical clinical applications. Furthermore, other interpretability techniques like Integrated Gradients, DeepLIFT, or Counterfactual Explanations could offer additional perspectives, and there are plans to explore them in future work. Similarly, the performance of LoRAS and ProWRAS can be assessed along with SMOTE. Conceptualization, M.W. and M.B.S. ; methodology, M.W. and M.B.S. ; software, M.W. and U.M. ; validation, M.W., M.B.S. and H.D. ([PERSON]); formal analysis, M.W., M.B.S., H.D. ([PERSON]) and S.S.; investigation, M.B.S. and H.D. ([PERSON]); resources, M.W. and U.M. ; data curation, M.W. ; writing--original draft preparation, M.W. and M.B.S. ; writing--review and editing, M.W., M.B.S., S.S., H.D. ([PERSON]), U.M. and H.D. ([PERSON]). All authors have read and agreed to the published version of the manuscript.