A Federated Learning Framework with Metaheuristic Optimization for Heart Disease Prediction

Due to lifestyle changes and daily behavioural routines of people living across the globe, cardiovascular diseases (CVD) are increasing in the modern world. In the treatment process, the prediction level of CVD is significantly required. Incorporating machine learning algorithms into CVD prediction can provide advantages such as reduced time consumption in the diagnostic process and improved decision-making. Hence, this research aims to implement a novel Lion-based Federated Learning for Disease Prediction (LbFLDP) technique to predict CVD. The novel approach includes three local hospital models and one centralized global model. The local models are trained using CVD dataset obtained from the kaggle website. After the training phase, the local models are used to predict CVD. These prediction features are then updated in the global model from the local models to enhance the prediction features in the global model. The global model is then initiated for predicting CVD. At this time, the performance of the suggested technique is evaluated in terms of accuracy, F-score, Precision, recall, and error rate. The proposed approach has 98.41 recall, 99.6% accuracy, 98.57 F-score, 98.57 precision, and 0.4% error rate.