A Federated Learning Approach for Cardiovascular Health Analysis and Detection

Environmental transition can potentially influence cardiovascular health. Investigating the relationship between such transition and heart disease has important applications. This study uses federated learning (FL) in this context and investigates the link between climate change and heart disease. The dataset containing environmental, meteorological, and health-related factors like blood sugar, cholesterol, maximum heart rate, fasting ECG, etc., is used with machine learning models to identify hidden patterns and relationships. Algorithms such as federated learning, XGBoost, random forest, support vector classifier, extra tree classifier, k-nearest neighbor, and logistic regression are used. A framework for diagnosing heart disease is designed using FL along with other models. Experiments involve discriminating healthy subjects from those who are heart patients and obtain an accuracy of 94.03%. The proposed FL-based framework proves to be superior to existing techniques in terms of usability, dependability, and accuracy. This study paves the way for screening people for early heart disease detection and continuous monitoring in telemedicine and remote care. Personalized treatment can also be planned with customized therapies.