## 4 Discussion
This analysis aimed to explore the impact of other features not selected by the BN model on the outcome. We found five individuals out of 106 (5%) who met these criteria, and none of them presented recurrent anxiety symptomatology. For instance, one case experienced \"Feeling nervous, anxious, or on edge\" (gad1) for more than half the days and \"Worrying too much about different things\" (gad3) nearly every day at baseline, yet did not present recurrent disorder. Another participant experienced gad for more than half the days and \"Feeling afraid, as if something awful might happen\" (gad7) nearly every day at baseline, but also did not present recurrent anxiety symptomatology. Additionally, a third individual experienced gad1, gad3, and gad7 for more than half the days each, and similarly did not present recurrent anxiety symptomatology. This further suggests that the focus should be on the features identified by the BN model. Although this study primarily focused on older individuals, the Depression group identified by our BN model is also recognised as a predictor of recurrent anxiety symptomatology, as demonstrated in another study [2], albeit in the general population. The Bayesian network model underperformed compared to Logistic Regression, SGD-Classifier, XGBoost, and RVM. However, it is noteworthy that feature selection was applied to the machine learning models, resulting in 10 selected variables, while the BN model, due to its nature of selecting features while learning the network structure, utilised only 4. Consequently, with fewer features, one might expect higher variance, but this also results in a less complex model with a reduced risk of overfitting, leading to improved generalisation on new, unseen data. Table 5 illustrates that although the standard deviation of the machine learning models was smaller, the difference between the performance on new unseen test data and the mean of the nested cross-validation remained higher than that observed in the BN model. Our approach offers notable advantages. While Logistic Regression can output probabilities for the outcome, it provides only a single point estimate. In contrast, the Bayesian network gives a probability distribution for the outcome, making it more robust for analysing uncertainties. Although the RVM uses Bayes' theorem and is a probabilistic model, it does not provide clear explainability. XGBoost offers explainability, but does not provide probabilistic measures, while the SGDClassifier provides neither explainability nor probabilistic measures. Another significant advantage of the BN model is its potential to capture complex dependencies between variables, which is not typically achieved by the other four models. Additionally, the BN model suggests a specific order of importance for the features, guiding the prioritisation of key factors to better identify individuals at risk of recurrent anxiety symptomatology. The network structure captures the joint probability distribution over all variables, enabling an analysis of how various combinations of factors influence the likelihood of recurrent anxiety. Furthermore, the model allows for the examination of predictors in isolation by marginalising the joint probability distribution. This approach enables the assessment of the isolated effect of specific variables without needing to remove others and rerun the model. This capability was demonstrated in the scenario simulations presented in Section 3.5. In summary, the BN model enhances explainability by highlighting key factors and providing insights through probabilistic measures. These measures explicitly show the impact of increasing or decreasing the values of specific features, offering a clearer understanding of the dynamics at play.