A Comprehensive Study Of The Machine Learning With Federated Learning Approach For Predicting Heart Disease

Abstract

Heart disease is a leading cause of mortality worldwide, resulting in millions of deaths annually. As individuals age and their physical condition deteriorates, the risk of developing heart disease increases. To mitigate this risk, predictive models leveraging machine learning and artificial intelligence have emerged as valuable tools for early diagnosis and treatment. In this review paper, we introduce the Google-pioneered concept of federated learning as a means to address concerns about data safety in the context of heart disease prediction. Federated learning, also known as collaborative learning, employs a technique wherein an algorithm is trained through multiple independent sessions, each utilizing its own dataset. This paper aims to provide a comprehensive investigation of recent machine learning approaches and databases employed in predicting the occurrence of cardiovascular disease.

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