Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

One of the deadliest diseases, heart disease, claims millions lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about patient and are subject to general privacy concerns, sharing is restricted under global laws. Furthermore, collection have a significant network communication cost lead delayed disease prediction. To address training latency, cost, single point failure, we propose hybrid framework at client end HSP consisting modified artificial bee colony optimization with support vector machine (MABC-SVM) for optimal feature selection classification disease. For server, proposed federated matched averaging overcome issues in this paper. We tested evaluated our technique compared it standard learning techniques on combined cardiovascular dataset. Our experimental results show that improves prediction accuracy 1.5%, achieves 1.6% lesser error, utilizes 17.7% rounds reach maximum accuracy.