# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 5 Discussion  
The results presented in this paper demonstrate that the proposed Stacking method for safety assessment of underground miners can effectively predict whether miners' health is at risk after training the model. The following findings were observed.  
The experimental results demonstrate that the model exhibits strong accuracy and generalization capabilities. By integrating vital sign data (e.g., heart rate, blood pressure, body temperature) from multiple sensors with environmental data (e.g., gas concentration, temperature, and humidity), potential health threats can be effectively identified, particularly in mining environments with challenging conditions.  
The accuracy of the evaluation model can be significantly improved by using industrial and mining data, along with vital sign data (e.g., heart rate, blood pressure, body temperature, age), as model inputs, and incorporating environmental data as auxiliary variables. This multi-dimensional data fusion method significantly improves the accuracy of health threat assessment and provides more comprehensive support for the safety management of miners.  
The model can predict common health issues (e.g., abnormal heart rate, abnormal body temperature, hypertension) and identify abnormal vital signs and environmental factors to detect potential safety risks in advance. For example, if the model detects that a miner's heart rate and blood pressure are above or below predefined thresholds, combined with data on harmful gas concentrations and temperature in the mining area, it can indicate that the miner may be at risk of health threats due to environmental factors, triggering an early warning alarm.  
At the same time, through the equipment positioning module worn by personnel, the position information of the threatened personnel can be obtained and handled in time.  
The practical application prospect of this model is broad, especially in high-risk working environments such as mines and construction sites. It is very important to find the health problems of workers in time and take effective intervention. Applying this model to a health monitoring system can provide more personalized and timely health assessment for miners, thus reducing the incidence of occupational diseases and accidents.  
The operating environment of a coal mine is complex. There is dust, high temperature, high humidity and other factors, which may affect the accuracy and stability of sensor data. In order to reduce the interference brought by environmental factors, deep learning models may be limited by computing resources, affecting real-time performance. Discussion of optimized computing performance can be added, such as the adoption of lightweight network architectures, edge computing architectures, and optimization strategies based on cloud-edge collaborative computing to ensure that systems can operate efficiently in resource-constrained environments. The acceptance degree of the intelligent evaluation system and the ease of use of the system will directly affect its promotion and application effect. Factors such as user training, interface friendliness and data privacy protection can be considered in the design of human-computer interaction systems to improve workers' willingness to use and trust the system.  
To sum up, the stacking algorithm model proposed in this paper has important application value in the health threat assessment of miners. Although there are still some challenges, with the continuous optimization of data quality and model algorithm, this model is expected to become an effective tool for future miner safety management and workers' health monitoring.