FedHFP: A Federated Deep Learning Framework for Heart Failure Prediction

Abstract

Heart failure emerges as one of the leading global causes of mortality, presenting a significant challenge in healthcare that necessitates early detection and intervention. Recent advances in Federated Learning (FL) have shown substantial potential in enhancing predictive analytics for heart failure, potentially enabling more timely interventions that could reduce the risk of heart failure. However, despite its promising potential, FL in healthcare is often complicated by technical challenges, such as model training across decentralized networks with limited infrastructure. Additionally, there is a gap in the literature, as no notable study has provided a comparative analysis of FL models under diverse settings. This paper proposes the FedHFP, a Federated deep learning framework for heart failure prediction, emphasizing its potential impact and utility in remote regions with limited healthcare access. Further, we present a comprehensive comparison of various deep learning models, including ANN, CNN, RNN, LSTM, and GRU, aimed at leveraging FL to optimize heart failure prediction. Through comparative analysis across diverse network configurations, the findings indicate that ANN may outperform other models, achieving an accuracy of ∼93.75%. Furthermore, our study emphasizes, at a conceptual level, the application of these findings to remote areas with limited healthcare infrastructure. The results underscore the efficacy of FL in healthcare predictive modeling and highlight its potential to mitigate communication overhead and preserve data privacy, particularly benefiting regions with limited healthcare infrastructure.