Smart IoT-enabled Healthcare Systems: Real-time Anomaly Detection and Decision Support using Deep Learning Models

Abstract

Smart healthcare systems that use Internet of Things (IoT) technologies are changing the medical field by letting data about patients be monitored and analyzed in real time. This paper suggests a new way to improve these kinds of systems by adding deep learning models to help find problems and make decisions.IoT devices are used in the suggested system to gather real-time information about things like vital signs, patient behavior, and surrounding factors. Deep learning algorithms, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are then used to handle this data and look for changes in the patient's health that aren't normal. These strange things could be signs of possible health problems or accidents, which would require quick action.Deep learning models are also trained on big datasets to find trends and connections in the data. This lets them help healthcare workers make decisions. For instance, the system can figure out how likely it is that a patient will get a certain illness by looking at their present health and their medical background.One of the best things about this method is that it can change and get better over time. More information is put into the models over time, making them more accurate and good at finding problems and giving useful information. This makes the method very useful for keeping an eye on people with long-term illnesses or finding diseases early. In adding deep learning models to healthcare systems that are connected to the internet of things (IoT) is a useful way to make patient care better. These tools could save lives and improve health by finding problems and helping people make decisions in real time.