# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
###### Abstract  
Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet's architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.  
federated learning; blockchain; heart disease; healthcare +
Footnote †: journal:  
data privacy and patient confidentiality are non-negotiable, the potential for federated learning is even more evident. The ability to collaboratively build predictive models for heart disease, a major global health concern, holds immense promise. However, creating such models while upholding privacy standards presents a significant challenge. This paper is a testament to the marriage of technological innovation and ethical considerations. At its core, it aims to develop a complete system for heart disease prediction while ensuring privacy is not compromised. This paper showcases the technical prowess of building predictive models and underscores the ethical imperative of safeguarding sensitive information. By combining innovation with integrity, the system presented here strives to be a cornerstone in reshaping how data are harnessed for predictive analytics while respecting privacy concerns. The key contributions of this work can be outlined as follows:  
* Techniques are explored to expand the available data pool without centralizing sensitive information. By integrating external datasets into the federated learning paradigm, the robustness of local models is enhanced.
* Differential privacy techniques are incorporated to strengthen data protection. This approach ensures that predictive insights do not compromise personal information, fostering participant confidence.
* The potential of blockchain technology and smart contracts is investigated to enhance transparency and trust. Their integration fosters accountability and provides an immutable record of interactions, redefining the governance of federated learning.
* A comprehensive system is presented by integrating data expansion techniques, advanced model architectures, privacy mechanisms, and blockchain technology, demonstrating the feasibility of predictive analytics in healthcare while upholding privacy principles.  
The remainder of this paper is structured as follows: Section 2 provides a literature review and a background on federated learning and blockchain technology. Section 3 details the data expansion techniques employed and describes the TabNet model architecture and implementation. Section 4 presents the results and analysis of the model's performance. Finally, Section 5 concludes the paper by discussing the findings and future research directions.