Deep Belief Network for Predicting Heart Disease Using Wearable Sensor Data

Heart disease remains among the most common causes of death, and it is even more crucial to devise the strategies to identify it at an early stage and monitor the health of individuals at any time. The research objective is to determine whether it can be possible to use wearable sensor data to predict cardiac disease. These sensors are able to detect the heart rate, blood pressure, and oxygen content in the blood. The raw sensor data can be analyzed to create a Deep Belief Network (DBN) that can be applied to generate correct predictions. This network achieves the best results by combining both supervised fine-tuning and unsupervised pre-training. There are several processes involved, including feature engineering, preprocessing, gathering data from wearables, and utilizing the DBN framework to sort it. Tests have shown that deep neural networks (DNNs) are superior to most other machine learning approaches for predicting the risk of heart disease and resilience. It is now possible to provide medical treatments more quickly, as both specificity and sensitivity have improved significantly. More and more research demonstrate that models powered by DBN can help patients recover while also reducing the cost of healthcare. This is achieved by monitoring heart health remotely and utilizing real-time data from wearable sensors. This new piece of information adds to the body of evidence.

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