## 5 Experimental Results and Analysis
### Prediction Results  
In image classification prediction research, standard lightweight neural network models include LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet. Lightweight neural network models such as LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet are relatively common lightweight neural network models that achieve SOTA performance. Therefore, in order to understand the experimental effect of the lightweight neural network model proposed in this article, we conducted comparative experiments with network models such as LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet. The \"Acute Ischemic Stroke MRI\" data set published on Kaggle is used in experiments in papers published by researchers such as [PERSON]. [PERSON]. Therefore, we chose to use this data set in the experiment, divided the training set, verification set, and test set according to the ratio of 8:1:1, and applied it to the comparative experiments of all models. When conducting experiments, we used all Adam optimizers; the batch_size settings were all 16, and the learning_rate settings were all 0.0001. The results of the experiment are shown in Table 4.  
Table 4 shows the experimental results on the public data set \"Acute Ischemic Stroke MRI\" introduced in this article. As seen from Table 4, in the experiment using public data sets to compare various network models, the EdgeNeXt and AFFNet network models achieved better experimental results. The lightweight neural network model proposed in this article has the best experimental results. When conducting comparative experiments, we accepted 350 MRI image data from two categories (with and without stroke risk, 175 images, respectively) provided by a medical institution. We organized them into a subject data set. This subject data set is used as a test data set for further testing of the model. To gain an in-depth understanding of the comparative experimental effects, we further tested each network model using the subject data set. The experimental results are shown in Table 5.  
As shown in Table 5, in the experiment using the subject data set to compare various network models, the EdgeNeXt and AFFNet network models achieved better experimen  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Basic Algorithm** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** & **AUC** \\ \hline LeViT [35] & 0.84 & 0.83 & 0.81 & 0.79 & 0.85 & 0.92 \\ EfficientNetV2 [33] & 0.94 & 0.93 & 0.92 & 0.91 & 0.95 & 0.95 \\ MobileViTv2 [36] & 0.86 & 0.84 & 0.85 & 0.83 & 0.87 & 0.93 \\ EdgeViTs [37] & 0.87 & 0.85 & 0.88 & 0.86 & 0.91 & 0.92 \\ EdgeNeXt [38] & 0.89 & 0.87 & 0.90 & 0.88 & 0.93 & 0.91 \\ AFFNet [39] & 0.93 & 0.92 & 0.91 & 0.89 & 0.94 & 0.96 \\ Linge & 0.96 & 0.95 & 0.93 & 0.95 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results of various network models on public data sets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Basic Algorithm** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1** & **AUC** \\ \hline LeViT [35] & 0.83 & 0.82 & 0.77 & 0.78 & 0.81 & 0.93 \\ EfficientNetV2 [33] & 0.95 & 0.94 & 0.91 & 0.93 & 0.92 & 0.94 \\ MobileViTv2 [36] & 0.85 & 0.87 & 0.80 & 0.81 & 0.83 & 0.94 \\ EdgeViTs [37] & 0.88 & 0.89 & 0.85 & 0.83 & 0.84 & 0.93 \\ EdgeNeXt [38] & 0.90 & 0.91 & 0.87 & 0.89 & 0.86 & 0.92 \\ AFFNet [39] & 0.92 & 0.93 & 0.89 & 0.91 & 0.90 & 0.95 \\ Linge & 0.97 & 0.96 & 0.94 & 0.96 & 0.97 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results of various network models on the subject data set.  
tal results. The lightweight neural network model proposed in this article has the best experimental results.  
This experiment shows that the lightweight neural network model proposed in this article can effectively support our proposed intelligent edge computing method for disease risk prediction.