Predictive Analytics for Cardiovascular Disease Diagnosis Using Federated Machine Learning

Abstract

Still the main cause of death globally, cardiovascular disease (CVD) demands exact and fast diagnosis for effective treatment. On the other hand, predicting cardiac disease is a challenging dance with seldom consistent diagnosis results. This article proposes a fresh approach based on federated machine learning (FML) for predictive analytics in CVD diagnosis. To preserve data privacy, the method aggregates information from different medical centres with FML techniques. The federated learning methodology permits cooperative model construction free from exchange of private patient data by training on dispersed data sources. The contribution is in the design of a cooperative, privacy-preserving framework for CVD prediction, therefore enhancing diagnosis accuracy and safeguarding patient anonymity. Since FML shows substantial improvement in prediction accuracy over conventional centralised approaches, results demonstrate that it is rather useful in CVD diagnosis. Since it helps to enable appropriate diagnosis and customised treatment strategies for cardiovascular illness, results suggest that FML has significant potential as a useful tool in healthcare analytics. Keywords are diagnosis, federated machine learning, cardiovascular disease, predictive analytics, privacy-preserving.