## 3 Results and Discussion
### Proposed Method Against Various Machine Learning Algorithms  
This subsection assesses the performance of different machine learning models in estimating the electric field strength of 5G base stations, including linear regression (LR), back-propagation neural networks (BPNN), and random forest regression models. A linear regression model, a basic machine learning technique, can automatically determine the optimal coefficients for the fitting function by minimizing RMSE during the training process. For the BPNN model, three key parameters are critical: the number of hidden layers, the number of nodes in each layer, and the learning rate. The learning rate plays a significant role in adjusting the step size for parameter updates in the BPNN. A high learning rate could cause the model to skip over optimal values, while a low learning rate might slow down the training process. To ensure that optimal parameter values are not missed, the learning rate is set to 0.001. Based on the prior ten-fold cross-validation discussion, three hidden layers are chosen, with the optimal number of nodes in each layer being 10, 8, or 2. Table 9 outlines the parameter settings for all machine learning models. A comparison is conducted between the values predicted by various machine learning models and the actual values. Table 10 presents the MAPE and RMSE for the different machine learning models. \begin{table}
\begin{tabular}{c c c} \hline
**Machine Learning Approach** & **Setting of Parameter** & **Time (s)** \\ \hline BPNN & MLPRegressor(HiddenLayerSizes = (10, 8, 2), \\  & LearningRate = 0.001) & 3962 \\ \hline Linear regression & LinearModel.LinearRegression () & 26.48 \\ \hline Random forest regression & RandomForestRegressor (\(\mathbb{N}_{1}=100,\ \mathbb{N}_{2}=5,\ D=19\)) & 3822.7 \\ \hline \end{tabular}
\end{table} 
Table 9: The various machine learning models’ parameter settings. It is evident that, compared to other models, the random forest regression model provides more accurate estimates of electric field intensity near 5G base stations, as indicated by its lower MAPE and RMSE. The complex relationship between features and electric field strength makes it challenging for a simple linear regression model to achieve accurate estimates. However, the BPNN model, with its multi-layered neurons, is better suited to capturing complex relationships and yields more accurate results than the linear regression model. Despite this, there is still a noticeable difference between the predicted and actual values, even with the BPNN. The random forest regression model, an ensemble learning approach, utilizes the Bagging algorithm and random feature selection to generate multiple decision trees. The final estimate is derived by averaging the outputs of these decision trees. Unlike single learners such as linear regression and BPNN models, the random forest regression model's inherent characteristics enhance its generalization ability, making it more resilient to anomalous measurement data. In addition to its suitability for estimating electric field intensity around 5G base stations, the random forest regression model offers further advantages, including support for parallel training and the elimination of costly data preprocessing. Given its superior performance and robustness compared to alternative models, the random forest regression model is highly recommended for the accurate prediction of electric field intensity near 5G base stations. The variation in uplink/downlink frequencies introduces differences in signal propagation and interference, which may influence the model's ability to generalize across scenarios. This variability can contribute to the observed MAPE of 5.98% and RMSE of 0.5928, especially if higher frequencies with less stable behavior are underrepresented in the training data. Feature importance analysis in the random forest model highlights the relative influence of each input variable on the predicted electromagnetic radiation levels.