Comparative Analysis of Deep Learning Models for IoT-Enabled Health Monitoring Systems
Abstract
Deep learning models have shown remarkable potential in enhancing IoT-enabled health monitoring systems by providing accurate and timely analysis of complex health data. This paper presents a comparative analysis of various deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), in the context of health monitoring applications. Using a large dataset of patient health records, we evaluate the performance of these models in terms of accuracy, computational efficiency, and scalability. The results demonstrate the strengths and limitations of each model, offering insights into their suitability for different health monitoring tasks.
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