# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
## 4 Experimental Results
### Improvement of Stacking Fusion Algorithm Based on the Network Search Algorithm  
In machine learning, hyperparameters must be tuned to enhance model performance and generalization. Commonly employed traditional methods for hyperparameter optimization include grid search and random search. However, their efficiency and performance are often constrained by the dimensionality and scale of the search space. To enhance both efficiency and performance, optimization can be conducted using a network search algorithm. This algorithm is a graph-based optimization technique designed to identify the  
Figure 10: Algorithmic model to evaluate ROC-AUC values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & **Blood Pressure** & **Heart Rate** & **Threatening** & **Threat Assessment** & **Predictive** \\  & **(mmHg)** & **(Beats/min)** & **Real Results** & **Results** & **Probability** \\ \hline
44 & 133 & 152 & 1 & 1 & 0.8761 \\
48 & 121 & 146 & 0 & 0 & 0.8638 \\
35 & 138 & 182 & 1 & 1 & 0.8555 \\
54 & 110 & 158 & 0 & 0 & 0.8547 \\
42 & 120 & 194 & 1 & 1 & 0.8521 \\
62 & 128 & 156 & 1 & 1 & 0.8512 \\
37 & 120 & 145 & 0 & 0 & 0.8507 \\
56 & 125 & 154 & 1 & 1 & 0.8496 \\
43 & 132 & 147 & 1 & 1 & 0.8418 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of threat assessment for miner operators. optimal solution within the hyperparameter space, thereby maximizing a given evaluation metric. This algorithm identifies the optimal hyperparameter combination by automatically constructing a graph-based representation of the search space and performing the search within this structure. It is formally defined as follows. Let the hyperparameter space be denoted as H, where each hyperparameter combination is represented as h \(\in\) H, and the corresponding evaluation metric is denoted as f(h). The basic procedure of the search is outlined as follows:  
* Defining the search space  
The hyperparameter space \(H\) usually consists of a combination of several hyperparameters, \(h\) = (\(h_{1}\), \(h_{2}\), \(\ldots\), \(h_{k}\)), where \(h_{i}\) denotes the value of \(i\) hyperparameter. Thus, the search space can be expressed as follows:  
\[H=\{(h_{1},h_{2},\ldots,h_{k})\mid h_{i}\in H_{i},i=1,2,\ldots,k\}\frac{1}{2} \tag{12}\]  
where \(H_{i}\) indicates the range of values for the hyperparameter \(i\). * Constructing a search map  
To construct the search graph automatically, it is essential to define the representation of the graph's nodes and edges. Typically, the nodes in a search graph correspond to hyperparameter combinations, while the edges represent the transformation relationships between these hyperparameters. Let the nodes u and v represent the hyperparameter combinations hu and hv, respectively. The edges connecting them can then be represented as:  
\[E(u,v)=\{(h_{u},h_{v})\mid f(h_{v})\geq f(h_{u})\} \tag{13}\]  
where \(f(h)\) represents the evaluation metrics for the hyperparameter combination \(h\). * Evaluation Nodes  
In the search graph, each node represents a set of hyperparameter values, and thus, each node needs to be evaluated to calculate its evaluation metric \(f(h)\). The evaluation of the nodes can be performed using methods such as cross-validation for model training and assessment. Termination conditions can be defined based on factors such as the number of evaluated hyperparameter combinations, the value of the objective function, and other performance metrics. Therefore, the network search algorithm must define termination criteria to determine when to halt the search process. In this study, we conduct hyperparameter tuning and cross-validation (CV) on the Stacking fusion algorithm model developed in previous work, using the Grid Search CV function. The objective is to identify the optimal hyperparameters, train the model on the training set, and evaluate its performance on the test set to enhance model accuracy. First, logistic regression and Random Forest are used as models to construct the Stacking model.