## 4 Discussion  
OSA is increasingly recognized as a significant concern within global health and economic contexts, underlining the importance of its early detection and diagnosis in the realm of preventive medicine [1,17,31]. The prompt identification of OSA is essential for initiating timely interventions, which can mitigate a broad range of associated health risks and enhance patient outcomes. Given that the standard diagnostic test for OSA, namely in-laboratory PSG, is expensive and often subject to long wait times due to high demand, there is a clinical imperative to identify the key factors and develop a simple yet reliable tool for estimating the OSA risk [17,18]. In general, BQ has an expectedly high sensitivity, as this tool has been developed for the identification of patients at a high risk of OSA in primary care settings. Despite this advantage, the BQ's low specificity and consequent high misclassification rate reveal its limited discriminatory capability, rendering its utility comparable to subjective clinical judgments [30,32]. In the quest for a straightforward questionnaire to ascertain OSA risk, clinicians are demanding enhancements to existing tools. [PERSON] et al. [33] posited that with certain modifications, the BQ could serve effectively as an OSA screening instrument. Furthermore, [PERSON] et al. [34] added to the growing body of evidence indicating the BQ's inadequacy in distinguishing between high- and low-risk patients, suggesting the need for the development of alternative protocols to heighten the diagnostic precision for such individuals. In this research, we sought to advance the capabilities of the traditional BQ through the integration of ML techniques. Our research integrates ML models with the standard BQ to harness Artificial Intelligence capabilities for analyzing patterns and correlations in data that might not be immediately apparent to human evaluators. This method facilitates a more detailed assessment of risk factors, potentially identifying the subtle signs of OSA risk overlooked by conventional approaches. To determine whether our ML-10 and the simplified two-item version ML-2 outperform traditional BQ in predicting patients at a high versus low risk of OSA, we conducted a comparative analysis using the established threshold used in the standard BQ (\(\text{AHI}\geq 5\)) and by comparing points in the ROC space. The findings underscore the efficacy in terms of sensitivity and specificity of the ML-10 model when contrasted with conventional BQ. A sensitivity of 93% at the same specificity as conventional BQ indicates that the model can correctly identify 93% of individuals (at low or high risk of OSA), operating with the same TN-rate. This result is significant as it demonstrates that, while maintaining the same rate of false alarms (1--Specificity), the ML-10 model is more effective in detecting OSA risk cases compared to conventional BQ. On the other hand, a specificity of 73% at the same sensitivity as conventional BQ emphasizes that the ML-10 model reduces the number of false positives (healthy individuals erroneously identified as at risk of OSA) compared to the conventional BQ, while still correctly detecting 82% of true positives. In this way, the ML-10 model shows excellent performance in identifying non-risk cases, surpassing conventional BQ. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \(\text{AHI}\geq 15\) & \(\text{AHI}\geq 30\) \\ \hline ML-10 & & \\ AUC & 85\% & 89\% \\ Sensitivity & 70\% & 69\% \\ Specificity & 81\% & 93\% \\ Accuracy & 77\% & 89\% \\ ML-2 & & \\ AUC & 83\% & 88\% \\ Sensitivity & 88\% & 60\% \\ Specificity & 69\% & 92\% \\ Accuracy & 76\% & 86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics comparison among ML-10 model and BQ-2 model in assessing OSA severity with the two cutoffs (\(\text{AHI}\geq 15\), \(\text{AHI}\geq 30\)). These results indicate that the ML-10 model surpasses conventional BQ both in terms of sensitivity (when specificity is maintained) and specificity (when sensitivity is maintained).