# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 3 Results
### Hyperparameter Optimization Results  
We use an Adam optimizer to optimize the model and search parameters using grid search. The final learning rate is \(1\times 10^{-4}\), dropout rate is 0.5, L2 penalty factor is \(1\times 10^{-4}\), epoch is set to 100, batch size is 10, and the balance parameter in the objective function is 0.006.  
Site features, as vectors quantifying site differences, are the basis for constructing the mapping relationship between representation learning features and site confounding factors. Only on this basis can MS-AINN be used to anti-interference classify the ASD. Therefore, in order to explore the impact of site feature dimensions on classification performance, this experiment tested the classification accuracy of site features extracted by MS-AINN with no AE and AE with different hidden dimensions under the ten-fold cross-verification of the whole site. Meanwhile, the results were compared with the results of the training model of norm neural networks, as shown in Figure 5. No AE means no dimensionality reduction with AE, and the mean vector of the one-dimensional functional connection feature vector of all the subjects in each site after flattening is calculated directly through site average pooling, which is taken as the site feature. The dimension is 19,900. AE Dimension represents the dimension of hidden layer when AE extracts site features, that is, the dimension of site features. It can be seen from Figure 5 that MLP, CNN (2D) and CNN (1D) have improved the accuracy of MS-AINN from 0.5% to 2.75% compared with norm neural networks under different site feature vector dimensions. When AE dimension 512 is used, the MLP accuracy rate increases by 1.97%, CNN (1D) by 2.34%, and CNN (2D)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ACC\%** & **AUC\%** & **Precision \%** & **Recall \%** & **f1\%** & **Site Classification ACC \%** \\ \hline CNN (1D) & 71.37 & 76.17 & 73.47 & 71.01 & 70.41 & 37.54 \\ MS-AINN-CNN (2D) & 74.12 & 77.10 & 74.79 & 73.97 & 73.78 & 18.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CNN (2D) anti-interference classification results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Year of Publication** & **ACC\%** \\ \hline GCN [23] & 2018 & 70.40 \\ ASD-DiagNet [11] & 2019 & 70.30 \\ Attention selection based on Extra-Trees [24] & 2020 & 72.20 \\ Self-attention deep learning framework [25] & 2021 & 72.48 \\ GCCN [12] & 2021 & 71.60 \\ AE combined with feature selection [26] & 2022 & 70.90 \\ MS-AINN-CNN (1D) & - & 75.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of ASD classification methods in multi-sites.  
by 2.75% to achieve the optimal performance of the corresponding model. The overall accuracy rate increases first and then decreases with the decrease of site feature dimension. The reason is that different parameters can obtain site features of different dimensions. When the dimension is large, there are more redundant features, and when the dimension is small, site differences cannot be fully expressed.  
In addition, the phenotypic information correlation features were selected for the site features extracted at AE dimension 512, and the top percentage features with the highest correlation ranking were selected to perform the site feature regression task. A threshold is defined based on the percentage that ranks high in correlation, and the threshold is used as a hyperparameter for optimization. The classification accuracy of MS-AINN-CNN (1D) under different thresholds is shown in Figure 6. The results show that as the feature selection threshold changes from low to high, the accuracy increases first, then decreases and then increases, and reaches the highest accuracy of 75.56% when the threshold is 0.3. With the change of feature selection threshold from low to high, AUC first increased, then decreased and then increased. When the threshold was 0.3, AUC reached the highest value of 78.99%.