# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 3 Introduction to Methods and Data Processing  
### Database  
The dataset, referred to as DATA, used in this study comprises 300 samples of human vital signs and historical data from the mining environment. The wearable device is employed to collect data on human vital signs, with blood pressure measured using the optical sensor integrated into the device. The blood vessels in the human body contract and expand with each heartbeat, resulting in variations in the absorption and reflection of light on the skin's surface. These variations are detected by the sensor and converted into electrical signals. Pulse waves, blood flow, and other physiological information are extracted through signal analysis. Body temperature is measured using a thermistor sensor,which exhibits a well-established relationship between temperature and resistance. As the body temperature changes, the resistance of the thermistor varies accordingly. The resistance change is monitored, and a built-in algorithm converts the thermistor's resistance into a body temperature value. The smart bracelet continuously monitors the wearer's heart rate in real time using photoplethysmography (PPG) technology, based on the photoelectric volume pulse wave. The system uses a light-emitting diode (LED) as the light source to emit light, and a light sensor measures the reflection changes from blood vessels, capturing the pulse fluctuations of each heartbeat. The heart rate is then calculated using a signal-processing algorithm. This study utilized diagnostic equipment (e.g., sphygmomanometer, thermometer) from the Affiliated Hospital of Hubei University for Nationalities to collect vital sign data, including blood pressure, heart rate, and body temperature, from 300 participants wearing smart bracelets, as shown in Table 1. Additionally, underground gas, temperature, and humidity sensors were installed to collect mining environment data. The model construction process is illustrated in Figure 2.  
Before constructing the threat assessment model, the collected dataset, referred to as 'data', must undergo preprocessing. The initial set of human physiological data may contain outliers caused by sensor errors, excessive noise, or other anomalies. Outliers can distort the results of data analysis and modeling. Therefore, it is necessary to remove them. A common method for outlier removal, based on prior conditions, is to utilize the mean and standard deviation. To address both global and local anomalies and achieve a better balance among different types of abnormal data, the LOF algorithm is employed for secondary data \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Age (Years)** & **Blood Pressure (mmHg)** & **Heart Rate (Beats/min)** & **Temperature (°C)** \\ \hline
1 & 27 & 130 & 142 & 36.5 \\
2 & 26 & 118 & 144 & 35.5 \\
3 & 40 & 118 & 162 & 35.1 \\
4 & 39 & 138 & 152 & 37.5 \\
5 & 30 & 122 & 144 & 37 \\
6 & 41 & 120 & 100 & 36.7 \\
7 & 33 & 126 & 126 & 35.8 \\
8 & 22 & 130 & 157 & 37.2 \\
9 & 20 & 120 & 140 & 36.2 \\
10 & 31 & 138 & 143 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset data. Figure 2: Digital twin (DT) model building. pre-processing. This enhances the robustness and accuracy of data processing, ensuring that the outlier detection method is effective across various data distributions. The data used in this study include human physiological data and work environment monitoring data, as shown in Figure 3. The raw data underwent preprocessing and feature engineering before being split into a training set, representing 70% of the total dataset, and a test set, representing 30%. The model was built using two algorithms: logistic regression and random forest. The predictions from both models were combined using the Stacking method. Parameter optimization was conducted through cross-validation and grid search to identify the optimal parameters, resulting in the development of the best-performing health threat assessment model. Logistic regression and random forest algorithms were used to train the models separately.