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One-third of all deaths worldwide yearly are attributable to cardiovascular disease (CVD). In contrast to the 7% of the wealthy who experience premature death, 43% of the poor do. Lifestyle diseases like obesity and diabetes are to blame. The importance of early identification of heart disease was demonstrated, and premature mortality was kept to a minimum. Combining clinical and biochemical data is essential for the early diagnosis of heart illness. Numerous IoT-enabled wearable healthcare applications have been created and released in recent years. Although the ability of wearable devices to share patient health data is expanding, it remains challenging to predict and identify health problems. Security, data storage, and patient monitoring are all part of the system. Artificial intelligence (AI) therapies may one day change the face of cardiology by providing doctors with cutting-edge data analysis and therapeutic decision-making resources. As the volume and complexity of data continue to increase, AI tools like machine learning (ML) and deep learning (DL) can assist medical professionals in learning more. Suppose we want to provide medical care to the elderly and those with chronic illnesses in the comfort of their own homes. In that case, we must upgrade our communication and information technology systems. The implemented DNN model's accuracy is amazing at 95.34 % and can yield other noteworthy outcomes when used to identify CVDs. We discuss and suggest the most suitable AI-IoT models for early CVD prediction and detection to reduce computational costs and increase time efficiency.
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