Publication

Abstract : IoT-based healthcare systems are creating a paradigm shift in patient management and monitoring systems. IoT-enabled devices are becoming increasingly common in hospitals, especially where patients require complex treatment, which helps to increase patient safety, efficient utilization of resources, and better health outcomes. This paper provides information about an IoT-based system for heart attack and fall detection and prevention systems for hospitalized patients. The platform integrates IoT device data and machine learning (ML) algorithms to forecast the likelihood of heart attacks, identify falls in real-time, and offer actionable recommendations to physicians to enhance the quality of care for patients. The unsatisfactory performance of current systems is related to the two models not integrating with each other or a lack of a unified framework that could address both focuses. Our proposed model, the IoT-Driven Fall and Heart Risk Prediction Model (IFHRM), is a unified framework that provides a better contribution to the healthcare field. Such an integrated framework offers great flexibility for risk management and decision support.