# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 3 Introduction to Methods and Data Processing
### Model Evaluation  
In stacking algorithms, model selection and training are critical, as they directly affect the performance and predictive accuracy of the final ensemble. Multiple base learners are trained on preprocessed data. To mitigate overfitting, techniques like cross-validation can be employed. Additionally, the weight of each base learner is determined using performance metrics such as accuracy, recall, and others. This paper selects the random forest and logistic regression algorithms as models to assess the health risks of personnel working as underground miners. The stacking algorithm, also referred to as cascading generalization, is an ensemble learning method that combines the predictions of multiple models to train a meta-model, resulting in more accurate predictions. In the stacking algorithm, the data are initially split \begin{table}
\begin{tabular}{c c c c c} \hline
**Serial Number** & **Age (Years)** & **Blood Pressure (mmHg)** & **Heart Rate (Beats/min)** & **Temperature (\({}^{\circ}\)C)** \\ \hline
1 & \(-0.964951\) & \(0.235731\) & \(1.498175\) & \(-0.037941\) \\
2 & \(-1.070332\) & \(-0.353498\) & \(1.590002\) & \(-1.201818\) \\
3 & \(0.492144\) & \(-0.353498\) & \(2.416447\) & \(-1.667369\) \\
4 & \(0.40449\) & \(0.628551\) & \(1.957311\) & \(1.125937\) \\
5 & \(-0.585565\) & \(-0.157089\) & \(1.590002\) & \(0.543998\) \\
6 & \(0.621123\) & \(-0.255293\) & \(-0.430197\) & \(0.194835\) \\
7 & \(-0.293255\) & \(0.039321\) & \(0.763557\) & \(-0.852655\) \\
8 & \(-1.483436\) & \(0.235731\) & \(2.186879\) & \(0.776774\) \\
9 & \(-1.664321\) & \(-0.255293\) & \(1.406347\) & \(-0.387104\) \\
10 & \(-0.468687\) & \(0.628551\) & \(1.544088\) & \(0.194835\) \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset Xdata. Figure 4: LOF algorithm outlier detection diagram. into a training set and a test set, with the training set employed to train multiple distinct models. In the stacking algorithm, models are constructed using different algorithms or parameter configurations, which can be either classification or regression models. Next, the models generate predictions using the test set, resulting in multiple outputs. These outputs are then used as input features for the meta-model, which are combined to produce the final prediction. The Stacking algorithm can be utilized in health threat assessments for miner operators to predict factors such as health status and safety risks. The core concept of the Stacking algorithm is to train a meta-model that learns to optimally integrate the outputs of base models, as illustrated in Figure 5. The algorithm generally comprises two phases; the first phase involves training the base models, and the second phase focuses on training the meta-model. Random forest is an ensemble learning technique that builds multiple decision trees and combines their predictions to produce a final result. It can be applied to both classification and regression tasks, including health threat assessment for miner operators. The model is as follows:  
\[y_{pred}=mode(\{y_{1},y_{2},\cdots,y_{n}\}) \tag{8}\]  
where \(y_{pred}\) represents the prediction of the random forest model, \(y_{n}\) denotes the predictions from the \(n\) individual decision trees, and mode is the function that computes the plurality, selecting the category with the highest frequency as the final prediction. The random forest algorithm is employed as a model for hazard assessment of underground miners, predicting the risk status of personnel. The model is constructed using personnel life characteristic data as input variables. Labels are assigned to each data point in both sets to indicate whether the personnel's health is at risk. The dataset consists of human body data, where \(X\) represents the feature set and \(Y\) represents the corresponding label set. A random seed is set to ensure result consistency. An instance of the random forest classifier model is created, and the training data along with corresponding labels are used to train the model.