Improving Heart Disease Risk Assessment with Advanced Deep Learning and Federated Learning

Abstract

Since heart disease is still one of the world's top causes of death, prompt therapies depend on precise risk prediction. This study investigates how to improve heart disease risk assessment by combining federated learning and advanced deep learning. For feature extraction and prediction, we suggest a novel architecture that makes use of deep neural networks, and federated learning allows distributed datasets to be securely collaborated on without sacrificing privacy. The potential of this strategy in developing scalable, privacy-preserving systems for individualized healthcare is shown by experimental results that show increased accuracy and generalization over conventional models. Our research paves the way for more accurate and comprehensive heart disease risk assessment.