## 5 Conclusions  
Given the substantial proportion of individuals still undiagnosed with OSA, coupled with the current absence of definitive diagnostic biomarkers for the condition, there is a pressing need for improved screening methodologies. The BQ, when enhanced with MLtechniques, stands out as a significant advancement in this regard. This study discovered that the ML-10 model was particularly effective in identifying individuals at risk of OSA with greater accuracy than the traditional BQ. By integrating ML techniques, we achieved a notable improvement in sensitivity and specificity, highlighting the potential of ML to refine diagnostic processes. This suggests that the ML-10 model can more effectively distinguish between high-risk and low-risk individuals, thereby reducing the likelihood of false positives and negatives. Furthermore, ML-2, with its reduced question set, also showcased its utility by maintaining slightly better diagnostic accuracy than the full BQ while offering a more streamlined and accessible screening tool. This adaptation could facilitate wider screening efforts, particularly in primary care settings or areas with limited access to sleep medicine specialists. Additionally, the flexibility of the classifier allows for adjustments across different operating points, enabling the selection of an optimal threshold that best balances sensitivity and specificity for the targeted population. This adaptability is crucial in tailoring the screening process to diverse clinical environments and patient needs, optimizing the early detection and management of OSA. Moreover, the application of the ML-10 model extends beyond the commonly used AHI threshold of the standard BQ (AHI \(\geq\) 5), demonstrating its utility across other clinically relevant AHI thresholds, specifically \(\geq\)15 and \(\geq\)30, which are frequently used in the literature to categorize the severity of OSA as moderate to severe, and severe, respectively. This versatility underscores the model's ability to adapt to varying clinical requirements, offering a nuanced approach to diagnosing OSA across its spectrum. Such adaptability ensures that the ML-10 model is not only a tool for preliminary screening but also a significant asset in stratifying OSA severity, thus enhancing the precision of diagnostic decisions and subsequent management plans. By leveraging these insights, healthcare professionals can better stratify individuals based on their risk levels, paving the way for more tailored diagnostic and management strategies for sleep apnea. ML-10 embodies the potential to transform the approach to diagnosing OSA, offering a more individualized assessment of risk. Looking forward, the insights gained from this research could serve as a foundation for further innovations in the field, ultimately leading to earlier detection, improved patient outcomes, and a reduction in the healthcare burden associated with OSA. These results can be achieved with minimal effort, because no modification to the BQ itself is necessary. The approach does not necessitate developing new questions or methodologies; instead, it leverages AI techniques to optimize an existing, widely used tool. This means that new screenings could achieve greater accuracy, and previously administered questionnaires could be easily re-examined using the ML-10 model. Consequently, more cases of OSA could be identified, and more healthy individuals could be correctly reassured. In the end, this study underscores the value of combining traditional clinical assessment tools with cutting-edge technology to address complex health challenges, marking a significant stride towards the future of personalized medicine in sleep health. Conceptualization, L.C., G.D.N., M.A., F.G., F.L., R.L., F.S. and C.P. ; methodology, G.D.N., L.C., P.A., L.D.B. and C.A. ; software, L.C. and G.D.N. ; validation, G.D.N., L.C., M.A., L.D.B. and P.A. ; formal analysis, G.D.N. and L.C. ; investigation, F.G., F.L., F.S., L.D.B. and M.A. ; data curation, L.C., G.D.N., F.G., F.L., F.S., L.D.B. and M.A.