A Comprehensive Review on Monitoring and Prediction Techniques for Heart Disease using Deep Learning and IoT

The most widespread research application of IoT in smart healthcare is health monitoring.IoT is a growing technology in the area of intelligent healthcare.World Health Organization (WHO) reported that 17.9 million deaths worldwide in 2019 were due to cardiovascular diseases (CVDs), or 32% of all death cases.85 percent of these deaths were caused by heart attacks and strokes.Early detection of CVDs is crucial for effective treatment with counseling and medication.It is a big challenge for healthcare industries to monitor and diagnose such amount of heart disease patients without any advanced technologies and devices.As heart disease patients are increasing day by day, biomedical data and the complexities in data also increasing.IoT combined with deep learning could be the way for exploiting and making use of complex patient data to monitor and diagnose the patients.Deep learning models along with IoT sensors can be used to process massive biomedical data related to patients and help doctors to monitor the patients meticulously and take decisions in real-time.In this article, First, we have discussed about heart disease and its current solutions, followed by a brief explanation of the internet of things (IoT), sensors and deep learning.Additionally, a thorough study of the most relevant and latest deep-learning methods for heart disease prediction are briefly reviewed.