Internet of Things and Deep Learning Integration in Healthcare: A Promising Path for Disease Prediction

The Internet of Things (IoT) improves our lives by facilitating real-time communication between people and objects. Predictive analytics could turn a reactive approach into a proactive one with the rise of artificial intelligence (AI) and machine learning in the healthcare sector. As a branch of machine learning, deep learning is able to deal with large amounts of data quickly, produce valuable insights, and solves complex problems. Accurate and timely diagnosis of diseases is essential for disease prevention and early treatment. The widespread adoption of electronic medical records necessitates the development of prediction models that are more accurate to effectively harness recurrent neural network variants of deep learning. This study investigates the integration of the Internet of Things and deep learning for disease prediction and diagnosis, which highlights key advancements in data collection, preprocessing techniques, and feature extraction strategies. The potentiality of convolutional and recurrent neural networks in increasing the accuracy of diagnosis and allowing early detection of diseases is analyzed. This review shows how IoT combined with deep learning can develop an investigative disruption in the prediction and diagnosis of diseases by expanding their validity, speed, and possibility of early detection thus opening the door to new applications and effective research.