Federated and ensemble learning framework with optimized feature selection for heart disease detection

Predictive models for early identification of heart disease must be precise and efficient because it is a major worldwide health concern. To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. In addition, federated learning was implemented to allow decentralized training while preserving sensitive medical data. The results highlight the effectiveness of combining these techniques in the detection of heart disease, providing a scalable and privacy-preserving solution for real-world healthcare applications. Two benchmark datasets were used to validate the proposed approach, ensuring the reliability and generalizability of the findings. Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. This distributed method allows model training without centralizing patient data, so it is compatible with strict data privacy rules. With up to 95% precision, our method shows a notable increase in prediction accuracy according to the testing results. This work offers a strong, scalable, and safe solution for the early identification of cardiovascular diseases by combining ensemble learning, feature selection, and federated learning, opening the way for more general uses in medical diagnostics.