Cardio vascular disease prediction by deep learning based on IOMT: review

The global burden of disease caused by cardiovascular diseases (CVDs) is increasing despite technical advancements in healthcare because of a dramatic rise in the developing nations that are experiencing rapid health transitions. The World Health Organization (WHO) estimates 17.9 million deaths worldwide in 2021 and is connected to CVDs, or 32% of all deaths. Since ancient times, people have experimented with the methods that extend their lives. The proposed technology is still a long way for attaining the aim of lessening the mortality rates. Early detection and proactive management of CVD risk factors are crucial for reducing the burden of these diseases. In recent years, researchers have been exploring the potential of deep learning methods for predicting cardiovascular disease risk depending upon data collected from IoMT devices. Deep learning (DL) methods used for cardiovascular diseases prediction have been popular in this domain. Several DL techniques are implemented to accomplish efficient prediction-based CVD. There are several steps in the CVD employing deep learning model. IoT sensors and deep learning techniques are used to process large amounts of patient-related biomedical data, enabling doctors to closely monitor their patients and make choices in real-time. An outline of the IoT, sensors, and deep learning is provided after a discussion of cardiac disease and its existing treatments. A complete analysis of the current and pertinent deep-learning techniques for heart disease prediction is reviewed. The result shows the performance metrics of the comparison of different deep learning approaches. This review is undertaken by pulling data from 44 papers published between the years 2020 and 2023, provides a thorough statistical analysis. Finally, this survey will be beneficial for CVD prediction researchers. [Graphics]