## 4 Discussion  
The decision to use the BN was influenced by the observation that, after incorporating more than four features, the AUC on the validation set began to decline (Figure 3), while the AUC on the training set continued to increase. This suggests overfitting of the model. Overfitting occurs when a model performs well on the training data but poorly on the test data, indicating it may not generalise effectively to new, unseen data. When we conducted predictions on the test data, we achieved an AUC of 0.821 and an F1-score of 0.743 (Table 5). These results closely align with those obtained through repeated cross-validation, falling within one standard deviation. This suggests that the model generalises well. It is expected that the training data yields higher values, as it is used to learn the parameters for making predictions. However, these values remain less than two standard deviations for F1-score. This further validates the robustness of the generalisation error estimated using repeated cross-validation with 100 evaluations. One practical application provided by the analysis in Figure 5 is its usefulness in identifying individuals based on the three symptoms of anxiety and the level of depressive symptomatology severity--the four key factors relevant to recurrent anxiety symptomatology shown in Figure 4. This can help in developing specific interventions for these individuals. If data and time are limited, clinicians should prioritise gathering information about gad2, as it has the highest probability of indicating recurrent anxiety symptomatology \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Min 1** & **Mean 2** & **Max 3** & **SD 4** & **Test 5** \\ \hline Logistic regression & 0.805 & 0.897 & 0.973 & 0.037 & 0.814 \\ SGDClassifier & 0.706 & 0.883 & 0.971 & 0.05 & 0.842 \\ XGBoost & 0.758 & 0.884 & 0.96 & 0.039 & 0.799 \\ RVM & 0.803 & 0.894 & 0.972 & 0.035 & 0.831 \\ Bayesian network & 0.648 & 0.836 & 0.959 & 0.056 & 0.821 \\ \hline \hline \end{tabular}  
* Min: Minimum AUC value for 100 evaluations: repeated cross-validation for Bayesian Network, nested cross-validation for the remaining models. 2 Mean: Mean AUC value for 100 evaluations: repeated cross-validation for Bayesian Network, nested cross-validation for the remaining models. 3 Max: Maximum AUC value for 100 evaluations: repeated cross-validation for Bayesian Network, nested cross-validation for the remaining models. 4 SD: Standard deviation of AUC values for 100 evaluations: repeated cross-validation for Bayesian Network, nested cross-validation for the remaining models. 5 Test: AUC value on the test data.  
\end{table} 
Table 6: Model performance comparison based on AUC metric. when the symptom occurs \"nearly every day\" (category 3 in Figure 5A. If constraints are less stringent, clinicians should prioritise information about both gad2 and gad6, as these combined have the highest probability of indicating recurrent anxiety symptomatology when both symptoms are in category 3 (Figure 5B). Next, information about gad4 should be prioritised, as combining this with gad2 and gad6 has the highest probability of indicating recurrent anxiety symptomatology when the symptom is in category 3 (Figure 5C). Finally, combining all three previous symptoms in category 3 with \"Depression group\" also in category 3 (PHQ score \(\geq\) 20) increases the probability of having recurrent anxiety symptomatology to 97.9%. Conversely, having category 0 (\"not at all\") for gad2, gad6, and gad4, and a PHQ score between 5 and 9 for the depression group, results in a probability of 0.4% for recurrent anxiety symptomatology. This analysis suggests a prioritised order of importance for the features to focus on. To gain a different perspective on the model, we analysed the test data for all individuals who had baseline values of 0 for gad2, gad6, and the Depression group. This indicates an absence of symptoms related to \"Not being able to stop or control worrying\", \"Becoming easily annoyed or irritable\", and a PHQ-9 score between 5 and 9.