# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
## 5 Conclusions  
Integrating technology with privacy considerations is essential in the evolving landscape of data-driven advancements. This paper explores heart disease prediction through federated learning, emphasizing the balance between innovation and ethical responsibility. By developing a comprehensive system incorporating data expansion techniques, the TabNet model, differential privacy mechanisms, and blockchain technology, we have demonstrated the potential to create effective predictive models while maintaining data privacy collaboratively. This work underscores the transformative power of federated learning, allowing institutions to tackle complex challenges collectively while safeguarding sensitive information. This paper's key contribution lies in integrating privacy-preserving techniques within predictive analytics. We successfully maintained high model performance without sacrificing privacy by applying the TabNet model and leveraging differential privacy. Blockchain technology enhanced transparency and accountability in the federated learning process, ensuring data integrity across institutions. The proposed approach was validated on real-world datasets (UCI Heart Disease and Cleveland datasets), where the model achieved high accuracy and balanced metrics under various privacy settings. Specifically, the best results were observed with 50 epochs and a privacy budget (\(\epsilon\)) of 6.855. This demonstrates that the method effectively balances privacy protection and predictive power.  
This method can be applied in healthcare institutions, such as hospitals and medical research centers, where collaborative data modelling is critical and privacy must be preserved. Several future research directions arise from this study. First, further exploration of advanced privacy-preserving techniques, such as combining differential privacy with homomorphic encryption, could enhance data security without compromising performance. Second, a comparative analysis between federated learning and non-federated approaches could provide insights into trade-offs between security, performance, and resource efficiency. Additionally, while the TabNet model has demonstrated promise, further investigation into its limitations is needed. Future work could focus on improving interpretability in real-world healthcare settings by incorporating explainable AI techniques like Shapley values or LIME. Moreover, exploring the scalability of TabNet in large-scale applications and comparing it with models like XGBoost or LightGBM could lead to more optimized solutions for handling larger datasets. A hybrid approach, combining TabNet's strengths with other models, could also enhance performance in large-scale deployments.  
Conceptualization, Y.O., C.H., E.H.S. and A.N.; Methodology, Y.O., C.H., E.H.S. and A.N.; Software, C.H.; Supervision, A.N.; Validation, Y.O. and E.H.S.; Visualization, Y.O. and C.H.; Writing--original draft, Y.O. and C.H.; Writing--review and editing, Y.O. and A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be available on request from the corresponding author.  
The authors declare no conflicts of interest.