Abstract

Adding Internet of Things (IoT) technology to healthcare systems has changed the way patients are cared for by letting them be monitored and data collected in real time. This essay looks at how deep learning can be used to improve IoT-enabled healthcare systems, with a focus on combining sensor data, making predictions, and coming up with ways to help.Sensor data fusion is a key part of putting together data from different sources, like medical equipment, smart tech, and electronic health records. Deep learning algorithms, especially CNN and RNN are very good at handling different types of data streams. This makes it possible to get a full picture of a patient's health. Healthcare professionals can get a full picture of a patient's health by combining information from many sources, such as bodily signs, exercise levels, and outdoor factors. Based on past data, predictive modeling uses the power of deep learning to guess what will happen with people's health in the future. IoT healthcare systems can predict how a disease will get worse, find risk factors, and suggest early treatment using methods like long short-term memory (LSTM) networks and attention mechanisms. These prediction models allow for quick treatments, methods for preventive care, and the best use of resources, which improves patient results and lowers healthcare costs in the long run.Deep learning also makes it easier to come up with smart management methods that are specific to each patient's needs. Machine learning algorithms can make personalized treatment suggestions and adaptable care plans by looking at real-time monitor data along with old patient records. These treatments could include changes to medications or lifestyles, or tips for medical workers. These give patients and healthcare staff more information to help them make better choices and better handle chronic conditions. When IoT technology and deep learning are combined, they have the ability to completely change the way healthcare is provided. IoT-enabled healthcare systems can improve patient tracking, analysis, and treatment by using advanced algorithms for sensor data fusion, predictive models, and smart actions. This leads to better quality of care and better health results.

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