This paper proposes a cloud-integrated system for analyzing ECG signals from wearable devices using deep neural networks. The model detects cardiac anomalies in real time and transmits alerts to medical professionals, ensuring proactive and responsive cardiac care for at-risk patients.

Marcus Henry Gallagher, Fiona Juliet Blackwell, Owen Douglas Carmichael, Abigail Rose Pickering, Thomas Gregory Yates

This study introduces a hybrid framework combining CNN and LSTM models for sleep stage classification using EEG, EMG, and ECG signals. The model enhances classification accuracy, providing a reliable tool for sleep disorder diagnosis in clinical and home-based health monitoring environments.

Lucas Alexander Whittaker, Daisy Eleanor Penrose, Henry Patrick Laughton, Matilda Florence Bennett, Edward Richard Bainbridge

This work introduces a blockchain-enabled solution for decentralized medical record sharing across healthcare institutions. It ensures data integrity, privacy, and consent-driven access using smart contracts and cryptographic techniques, streamlining secure collaboration between patients and healthcare providers globally.

James Oliver Radcliffe, Florence Clara Townsend, Noah Jeremy Redgrave, Amelia Beatrice Harcourt, George William Merton

This paper presents a deep ensemble strategy combining multiple CNN architectures with transfer learning to detect COVID-19 from chest CT images. The proposed model demonstrates high sensitivity and specificity, providing a valuable diagnostic tool for pandemic response and hospital triage systems.

Jonathan David Hollingsworth, Isabella May Linton, Alexander Hugh Chamberlain, Sophia Grace Deverell, William Thomas Endicott

This paper presents an interpretable AI model for predicting diabetes progression based on electronic health records. SHAP values are used to explain model outputs, providing actionable insights for clinicians and promoting trust in AI-driven decision-making processes in personalized diabetes care management.

Edward Nathaniel Balfour, Imogen Rose Fitzgerald, Leo Benedict Ainsworth, Charlotte Emily Prestwich, Hugo Frederick Stapleton

This study proposes a cloud-enabled diagnostic framework integrating MRI analysis and cognitive metrics for early Alzheimer’s detection. The model utilizes ensemble learning and neuroimaging biomarkers to enhance diagnosis precision and promote early intervention in remote and clinical healthcare environments.

Julian Edward Cartwright, Alice Victoria Pennington, Nathaniel George Huxley, Clara Josephine Wilton, Henry Maxwell Hargreaves

This paper explores a federated learning framework for hospital collaborations in clinical predictions without compromising data privacy. By training models locally and aggregating updates globally, the architecture ensures compliance with privacy regulations while maintaining high model accuracy across institutions.

Frederick James Braithwaite, Amelia Jane Blackstone, Oliver Sebastian Westgate, Beatrice Charlotte Ellington, Samuel Henry Langton