IoT-Enabled Predictive Healthcare Monitoring Using Machine Learning Models

This study explores the capabilities of integrating the Internet of Things (IoT) with machine learning to monitor health in real time. Using IoT-powered devices like heart rate monitors, glucose meters, and blood pressure devices, health metrics were consistently gathered and sent to a cloud-based system. After refining the data for better quality, machine learning techniques were used to create health prediction models. The findings showed significant precision, especially when using neural networks, in predicting health-related events. This combined system can identify and notify healthcare experts about potential health concerns immediately. However, the promise of ongoing health tracking and early detection comes with concerns regarding data protection, data handling, and model reliability.