article · Alexandria Engineering Journal
Remote healthcare monitoring plays a vital role in managing long-term conditions such as high blood pressure, which significantly increases the risk of heart disease in older populations. A framework combines physical data from routine medical monitoring with electronic clinical records to improve the precision of cardiac risk prediction. The architecture uses Internet of Things technology alongside advanced computational models. The Extreme Gradient Boosting algorithm evaluates large datasets and identifies key diagnostic features, while a Bidirectional Long Short-Term Memory deep learning model captures intricate temporal patterns across patient records over time. When tested against alternative classifiers, including naive Bayes, decision trees, and random forests, this combined predictive approach achieved an overall accuracy rate of 99.4 per cent, demonstrating strong performance for ongoing health tracking.
Managing chronic conditions like high blood pressure is essential for preventing cardiac events, particularly in elderly individuals. By combining remote connected sensors with advanced machine learning models, healthcare systems can monitor patient risks continuously. Delivering predictive accuracy above 99 per cent offers clinicians and caregivers a more reliable method to identify early deterioration, potentially reducing emergency hospitalisations and improving long-term care management.
This technology could enable automated alert and remote monitoring software for healthcare providers, telemedicine operators, and residential care facilities managing chronic cardiovascular risks. Given that the abstract details model design and comparative algorithmic benchmarking achieving 99.4 per cent accuracy without clinical trials or field deployment data, the work appears to be at an applied and tested, algorithmic validation stage prior to commercial system integration.
AI-generated from the published abstract. Always read the original work before citing.
The advancement of medical technology has brought about a significant transformation in remote healthcare monitoring, which is crucial for providing customized care and ongoing observation. This is especially important when it comes to controlling long-term illnesses like high blood pressure, which raises the risk of heart disease considerably, especially in older people. This methodology achieves greater accuracy by combining regular medical monitoring and Electronic Clinical Data (ECD) from complete medical records with physical data from patients' routine medical monitoring. This innovative technique enhances the area of cardiac disease prediction. A technique that uses cutting-edge machine learning models and IoT technology to meet this demand. In particular, we use the powerful Extreme Gradient Boosting (XGBoost) algorithm to effectively examine big datasets and extract important characteristics to improve prediction accuracy. The deep learning model Bidirectional Long Short-Term Memory (Bi-LSTM) is used to further enhance prediction skills to extract complex temporal patterns from patient data. It outperformed naive Bayes, decision trees, and random forests with our approach, achieving a greater prediction accuracy of 99.4 %. With the combination of Internet of Things technologies and sophisticated machine learning models, this paper offers a novel approach to remote healthcare monitoring.
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DOI: 10.1016/j.aej.2024.06.036
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