Heart Disease Prediction using Federated Learning

Heart disease continues to be the leading cause of death worldwide, necessitating the development of efficient and reliable predictive models to aid in early diagnosis and prevention. Traditional approaches to developing such models frequently encounter data privacy and scalability issues, especially when dealing with sensitive medical information spread across multiple institutions. Federated learning emerges as a promising approach for collaborative model training across decentralized data sources, eliminating the necessity for raw data sharing. In our research, we introduce a federated learning model to predict heart disease, drawing on insights from various hospitals or healthcare facilities while safeguarding data confidentiality. We employ a federated averaging algorithm to train a centralized model on locally stored patient data, ensuring that sensitive information remains secure within each participating entity. The proposed approach demonstrates competitive predictive performance compared to centralized models trained on pooled data, while addressing concerns related to data privacy and regulatory compliance. Through experimentation on real-world healthcare datasets, we validate the effectiveness and feasibility of our federated learning approach for heart disease prediction, paving the way for scalable and privacy-preserving predictive analytics in the medical domain.