# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results
### Model Performance Metrics
Most notably, the hybrid CNN-RNN-transformer architecture outperformed all other models across every metric, achieving an average accuracy of 99.898%, perfect precision, and F1-scores consistently above 0.9999. Although the numerical improvement over the spatiotemporal transformer was marginal (\(\lesssim\)0.01%), it confirms the added value of combining convolutional, recurrent, and attention-based mechanisms within a single unified framework. These findings highlight the hybrid model's superior diagnostic robustness, interpretability, and generalization capability in EEG-based ADHD classification. To further validate these findings and quantify the degree of performance stability across data partitions, Table 3 provides a comprehensive summary of the mean and standard deviation values for each evaluation metric. By explicitly presenting both average values and variability across five stratified folds, this table enables a more nuanced comparison of generalization behavior. As presented in Table 3, the repeated train-test experiments clearly demonstrate substantial differences in generalization stability across the evaluated architectures. The \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Fold** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline Hybrid CNN–RNN–Transformer & Fold 1 & 0.9998 & 1.0000 & 0.9998 & 0.9999 & 1.0000 \\  & Fold 2 & 0.9998 & 1.0000 & 0.9999 & 0.9999 & 1.0000 \\  & Fold 3 & 0.9999 & 1.0000 & 0.9999 & 0.9999 & 1.0000 \\  & Fold 4 & 0.9998 & 1.0000 & 0.9999 & 0.9999 & 1.0000 \\  & Fold 5 & 0.9999 & 1.0000 & 0.9999 & 0.9999 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline CNN–LSTM & 92.36 \(\pm\) 0.79 & 0.906 \(\pm\) 0.011 & 0.932 \(\pm\) 0.011 & 0.919 \(\pm\) 0.010 & 0.941 \(\pm\) 0.005 \\ CNN–GRU–LSTM & 99.63 \(\pm\) 0.04 & 0.998 \(\pm\) 0.0006 & 0.994 \(\pm\) 0.0011 & 0.996 \(\pm\) 0.0004 & 0.9998 \(\pm\) 0.0001 \\ Transformer Encoder (2\(\times\)) & 75.85 \(\pm\) 1.59 & 0.813 \(\pm\) 0.0556 & 0.684 \(\pm\) 0.0692 & 0.409 \(\pm\) 0.0278 & 0.845 \(\pm\) 0.0169 \\ Transformer Encoder (3\(\times\)) & 74.26 \(\pm\) 2.11 & 0.768 \(\pm\) 0.0430 & 0.703 \(\pm\) 0.0372 & 0.415 \(\pm\) 0.0134 & 0.825 \(\pm\) 0.0242 \\ Spatiotemporal Transformer & 99.88 \(\pm\) 0.11 & 0.9996 \(\pm\) 0.0001 & 0.9997 \(\pm\) 0.0001 & 0.9998 \(\pm\) 0.0001 & 0.9999 \(\pm\) 0.00015 \\ Hybrid CNN + GRU + LSTM & 99.63 \(\pm\) 0.04 & 0.998 \(\pm\) 0.0005 & 0.994 \(\pm\) 0.0010 & 0.996 \(\pm\) 0.0004 & 0.9999 \(\pm\) 0.0000 \\ Hybrid CNN–RNN–Transformer & 99.99 \(\pm\) 0.005 & 1.0000 \(\pm\) 0.0000 & 0.9999 \(\pm\) 0.00004 & 0.9999 \(\pm\) 0.00003 & 1.0000 \(\pm\) 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average \(\pm\) standard deviation of classification metrics across 5-fold cross-validation for all models. transformer encoder models (2\(\times\) and 3\(\times\)) exhibited the highest performance variability across splits, particularly in F1-score (\(\pm\)0.0278 and \(\pm\)0.0134, respectively) and recall (\(\pm\)0.0692 and \(\pm\)0.0372), indicating that their classification outcomes are highly sensitive to the specific train/test partitioning. For instance, the transformer encoder (2\(\times\)) achieved an average accuracy of 75.85% \(\pm\) 1.59, while the CNN-GRU-LSTM model consistently reached 99.63% \(\pm\) 0.04, reflecting a performance gap of nearly 24 percentage points, with approximately 40\(\times\) higher standard deviation for the transformer-based model. These findings support the concern that earlier reported high transformer performance was not representative of general behavior. The transformer encoder (3\(\times\)) further demonstrated poor generalization, achieving the lowest AUC among all models (0.825 \(\pm\) 0.0242) and exhibiting instability in both recall and F1-score.