A proposed IoT and machine learning-based remote ECG monitoring system aims to enable continuous real-time cardiac diagnosis and personalized management.
The prevalence of cardiovascular diseases as a source of sickness and mortality around the world calls for early detection and efficient management for better patient outcomes and lower health care costs. The limits of traditional diagnostic techniques, such as electrocardiography (ECG), are brought on by the need for specialized equipment and qualified medical professionals. To enable remote cardiac illness diagnosis and management, this study introduces a new smart health monitoring system that makes use of ECG sensors, the Internet of Things (IoT), and machine learning. The system uses wireless sensors to continually gather real-time ECG data from patients. It then sends this data to a cloud server via an IoT gateway where it is analyzed by a machine learning model that has been trained on different heart conditions. Following the development of customized treatment plans based on the specific needs of each patient, healthcare practitioners are instantly alerted to any potential issues discovered through forecasts. The system's accuracy and dependability are rigorously evaluated using real data, and ethical and legal issues are painstakingly taken into account to safeguard patient autonomy and privacy. With the potential to improve patient outcomes, lower healthcare costs, enable remote patient monitoring, and support individualized care, this suggested system has the power to change the diagnosis and treatment of cardiac disease.
Rane et al. (Wed,) studied this question.
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