## 7 Results and Discussion  
In this section, we elucidate the outcomes derived from various modeling techniques and their comparative performances. These results represent a culmination of our rigorous efforts to optimize model efficiency, particularly when dealing with the vast and complex FAERS dataset. The main objective was to improve the predictive accuracy of a model on a specific dataset by exploring different techniques, such as ascending feature selection and layer-static-weighted attention, and combining them to outperform the baseline model. The main goal was to optimize the model's accuracy and ability to predict outcomes based on  
Figure 10: Attention weight histogram at different \(\alpha\): (**a**) \(\alpha\) = 0.1; (**b**) \(\alpha\) = 0.5; (**c**) \(\alpha\) = 0.9.  
the features in the dataset, especially natural language processing (NLP) and demographic features. 1. Baseline Model: Our foundational model, hinged solely on NLP features capped at 5000 features, marked a validation accuracy of 70.27%. Concurrently, it registered a test accuracy of 70.28%, including all extracted NLP features without filtering out any feature. 2. Ascending Feature Selection: With the deployment of ascending feature selection, there was an observable progression in the model's performance commensurate with the amplification of features. Table 3 presents the results of an ascending feature selection process, detailing how the average validation and test accuracies evolve as the number of features increases from 500 to 5000. Initially, both accuracies improve as more features are added, reflecting the model's enhanced ability to generalize from the training data to unseen data. This trend continues up to 4000 features, beyond which we observe a plateau and even a slight decline in performance when reaching 4500 features, indicating a point of diminishing returns. Despite incorporating the largest set of features, the model with 4500 features does not achieve the best performance according to the provided metrics. The peak in performance occurs with 4000 features, where both validation and test accuracies are the highest. The slight decrease in accuracy with 4500 features suggests that the model may be overfitting, capturing noise, or irrelevant patterns in the additional data. Therefore, the model with 4000 features emerges as the optimal choice, striking a balance between high accuracy and avoiding the complexity and potential overfitting associated with the maximum number of features. This model provides effective and reliable predictions without unnecessary complications. To further enhance the model's performance, especially on the optimal set of 4000 features, implementing a \"layer-static-weighted attention\" mechanism could be the next stage. Layer-static-weighted attention is an advanced technique in deep learning models, particularly useful in complex models, where it allows the model to focus more effectively on the most informative parts of the input data. By weighting the importance of different layers' outputs, this approach can help in capturing more nuanced patterns and relationships in the data, potentially improving the model's accuracy and generalization ability. This technique is especially promising for refining the model's understanding and processing of the optimal feature set, thereby potentially surpassing the current state-of-the-art results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No of Features** & **Average Validation Accuracy** & **Average Test Accuracy** \\ \hline
500 & 69.272\% & 69.259\% \\
1000 & 69.899\% & 69.893\% \\
1500 & 70.132\% & 70.135\% \\
2000 & 70.220\% & 70.214\% \\
2500 & 70.251\% & 70.250\% \\
3000 & 70.267\% & 70.271\% \\
3500 & 70.279\% & 70.279\% \\
4000 & 70.283\% & 70.286\% \\
4500 & 70.273\% & 70.285\% \\
5000 & 70.270\% & 70.280\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ascending feature selection. the optimal decided based on the highest validation accuracy of 74.54%.