Real-Time Disease Detection: Building IoT Remote Monitoring Systems with Neural Networks

The integration of Internet of Things (IoT) technologies with artificial intelligence is revolutionizing healthcare by enabling real-time disease detection and patient monitoring. This study presents the design and implementation of an IoT-based remote health monitoring system that utilizes neural networks for early disease diagnosis. Biomedical sensors continuously capture vital parameters such as heart rate, body temperature, oxygen saturation, and electrocardiogram signals, which are transmitted via IoT gateways to cloud servers for intelligent analysis. A deep neural network (DNN) model processes the sensor data to identify abnormal patterns associated with potential diseases, achieving high accuracy in predictive diagnostics. The proposed framework demonstrates scalability, low latency, and cost-effectiveness, making it suitable for rural healthcare and telemedicine applications. By combining real-time data acquisition with machine learning inference, this research establishes a foundation for next-generation smart healthcare systems that ensure timely intervention and improved patient outcomes.