PREDICTION OF HYPERTENSION RISK IN HUMAN USING FEDERATED LEARNING

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dc.contributor.authorHAFSA MANZOOR
dc.date.accessioned2025-08-28T12:33:52Z
dc.date.available2025-08-28T12:33:52Z
dc.date.issued2024
dc.description.abstractHypertension, commonly known as high blood pressure, when the force of the blood on the artery walls is continuously too great, it is referred to as hypertension. This research employs federated learning along with machine learning techniques. Predictive models based on a large dataset of various blood pressure metrics from hypertensive patients are developed and evaluated. The study thoroughly evaluates the performance of federated learning techniques when paired with traditional machine learning approaches in order to predict patterns of hypertension. Federated learning in particular, with its accuracy rate of 99.4%, has great potential for healthcare applications. With a heavy focus on privacy and the use of decentralized data sources, the federated learning methodology shows promising results, improving the accuracy and generality of hypertension prediction significantly. The results emphasize how important it is to incorporate information into models that forecast hypertension. They also show how related literacy has revolutionary potential in healthcare applications, especially when it comes to enhancing prediction accuracy and protecting privacy. This research provides important new understandings of hypertension, providing a solid basis for improving public health initiatives and guiding policy choices.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/5638
dc.language.isoen
dc.publisherUMT, Lahore
dc.titlePREDICTION OF HYPERTENSION RISK IN HUMAN USING FEDERATED LEARNING
dc.typeThesis
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