Why the study?
Cardiovascular disease is a leading cause of global mortality, but early risk prediction can be achieved through ML and AI-based predictive models combined with IoT devices.
This review highlights the potential of combining federated learning with IoT wearables to enhance cardiovascular disease prediction while addressing privacy concerns.
Should not yet change CVD monitoring practice; leaves open need for prospective validation of federated IoT models.
Cardiovascular disease is one of the most significant causes of mortality worldwide, considering the lives of millions of humans each year. The chance of getting cardiovascular disease rises as people’s life style and their physical health declines. Prediction of this risk can now be achieved by early identification and treatment with the use of ML and AI-based predictive models. With regard to the prediction of cardiovascular disease events in conjunction with IoT devices along with ensemble models combined with federated learning model is suggested to get more performance. This paper explores various methodologies to predict cardiovascular events, emphasizing federated learning (FL) and its application in CVD detection. This paper also reviews the advantages and challenges of federated learning in healthcare along with IoT wearables features & applications.
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Rao et al. (2024) studied this question.
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