Research on the Analysis and Prediction Model of Health Monitoring Data Based on the Internet of Things

This research centers on the development of an advanced model for analyzing and forecasting elderly health monitoring data, leveraging the Internet of Things (IoT) technology. A comprehensive health monitoring system specifically designed for the elderly is established, enabling the real-time acquisition and transmission of daily health metrics through the integration of IoT technologies. During the data analysis phase, an association rule mining algorithm is utilized to identify hidden patterns and correlations within the elderly health data, thereby facilitating the prediction of health outcomes. A subsequent health prediction model is formulated, incorporating data mining and machine learning methodologies, with its effectiveness and reliability rigorously assessed through simulation experiments. The outcomes of these simulations demonstrate a significant enhancement in both the precision and efficiency of the prediction model. This progress provides a solid scientific basis for the health management of the elderly population. Further model simulations confirmed the accuracy and practicality of the developed prediction model, underscoring its extensive potential for application in elderly health management.