A Research Study of Heart Health Monitoring Using Deep Learning and IoT

Cardiovascular diseases (CVDs) pose a substantial worldwide health burden, hence requiring sophisticated strategies for their prevention and treatment. This research study presents a novel framework that combines deep learning algorithms and IoT devices to enable ongoing monitoring of heart health by means of heartbeat categorization. The objective of this project is to create a highly accurate and effective system that can classify different cardiac diseases based on real-time electrocardiogram (ECG) signals. This study makes a scholarly contribution by utilizing the capabilities of deep learning and the IoT to improve the timely identification and proactive treatment of cardiovascular conditions. As a result, patient care and overall health outcomes are enhanced. The paper presents a thorough technique that includes many stages such as data collecting, preprocessing, building of a deep learning model, integration of IoT, and evaluation of performance. The expected results include the development of a comprehensive categorization system, the integration of Internet of Things (IoT) technology for continuous monitoring, and a comparative analysis demonstrating the advantages of the system in comparison to current methodologies. In this undertaking, our aim is to achieve significant advancements in the field of cardiac health monitoring and disease identification, hence promoting enhanced healthcare methodologies and patient results.