IoT-Powered Predictive Analytics for Effective Asthma Exacerbation Management Using Linear Regression Models

To improve patient outcomes and protect healthcare costs, it is essential to treat asthma exacerbations effectively. This research aims to understand better how to treat asthma exacerbation using the Internet of Things (IoT) and predictive analytics. It presents a system that uses IoT sensors to track vital signs like heart rate, breathing rate, temperature, humidity, and air quality in real-time. Linear regression (LR) models process the data to foretell when an exacerbation might happen. The prediction model's ability to recognize patterns and connections between asthma symptoms and environmental factors makes personalized treatment and prompt interventions possible. The findings show that exacerbation predictions are much more accurate when IoT and predictive analytics are combined, which allows for proactive management techniques. Healthcare practitioners are equipped with actionable information for improved asthma control, and this strategy also enhances patient safety. According to the research, IoT predictive analytics can revolutionize asthma treatment by facilitating data-driven decisions.