Abstract

The integration of Internet of Things (IoT) devices in healthcare systems has revolutionized patient monitoring and disease prediction capabilities. This study presents a novel hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for real-time disease prediction in IoT-based healthcare environments. Our proposed model processes continuous physiological data streams from wearable sensors, environmental monitors, and medical devices to predict potential health deteriorations before clinical manifestation. The hybrid architecture achieved 94.7% accuracy in cardiovascular disease prediction, 91.3% in diabetes complications, and 88.9% in respiratory disorder detection across a dataset of 15,000 patients over 18 months. The model demonstrates superior performance compared to traditional machine learning approaches, with reduced false positive rates (6.2%) and enhanced real-time processing capabilities (average response time: 1.2 seconds). This research contributes to the advancement of predictive healthcare analytics and establishes a foundation for proactive medical intervention systems.