## 7 Results and Discussion
This balanced approach, with equal weightage to NLP-sourced and demographic features, results in the highest test accuracy of 74.56% and a closely matched cross-validation (CV) ROC score of 74.49%. The data indicate that the model achieves optimal performance at this equilibrium, underscoring the significance of a harmonized attention mechanism for both types of features. The layer-static-weighted attention mechanism enhances the model's ability to extract and prioritize relevant information from both feature sets, leading to a more effective and discriminating predictive model. Such a targeted focus on maintaining an equal importance level across diverse feature types not only improves the model's accuracy but also its generalizability. This is particularly evident in the consistent increase in performance metrics up to the 50% attention weight mark, after which the performance begins to plateau and then slightly declines. This pattern suggests that an overly skewed focus toward one type of feature over the other diminishes the model's effectiveness, highlighting the critical role of balanced attention in achieving superior model performance. The performance with the amalgamation of ascending feature selection and layer-static-weighted attention remarkably transcended that of the baseline model. This augmentation not only epitomizes the potency of a synergized approach but also accentuates that an astute capitalization on the comprehensive nuances of the FAERS dataset, coupled with a meticulous calibration of the model's emphasis on varied feature dimensions, can be instrumental in fortifying predictive accuracy. The evaluation of the classification model's ability to discern the seriousness of drug events was comprehensively carried out by employing precision-recall (PR) and receiver operating characteristic (ROC) curves, showcased, respectively, in Figures 11 and 12. The precision-recall curve elucidated in Figure 10 presents an impressive area under the curve (AUC) of 0.83, denoting a commendable balance of precision and recall within the classification task, thereby underscoring the model's proficiency in accurately classifying serious and non-serious drug events. Meanwhile, the ROC curve depicted in Figure 11 presents an AUC of 0.83, signifying a respectable level of discrimination, although it also hints at potential areas for refinement, notably in diminishing the rate of false positives. Further elucidation of the model's classification performance is provided with a confusion matrix, which reveals a true-positive rate, or sensitivity, of 75.76% for predicting serious drug events and a true-negative rate, or specificity, of 73.19% for non-serious drug events. Figures 11 and 12 align closely with the precision and recall values of 0.76 for the positive class, suggesting the model's balanced competency in identifying both serious and non-serious events. The classification report sheds light on nuanced aspects of predictive performance: The model demonstrates an equal capacity, as indicated by an F1 score of 0.73, for predicting non-serious events, while it exhibits marginally superior precision and recall values of 0.76 for serious event predictions. Collectively, the model attains an overall accuracy of 0.75, with both macro and weighted averages for precision, recall, and F1 scores hovering around 0.74 and 0.75, reflective of the model's robustness and uniform \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attention Weight** & **Average Validation Accuracy** & **Average Test Accuracy** \\ \hline
10 & 74.456\% & 74.464\% \\
20 & 74.519\% & 74.527\% \\
30 & 74.536\% & 74.545\% \\
40 & 74.541\% & 74.548\% \\
50 & 74.544\% & 74.560\% \\
60 & 74.537\% & 74.554\% \\
70 & 74.539\% & 74.559\% \\
80 & 74.526\% & 74.546\% \\
90 & 74.528\% & 74.550\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of layer-static-weighted attention (on optimal 4000 features). performance across the dual categories of drug event severity.