## 4 Discussion
This implies that, depending on clinical or screening needs, the ML-10 model can be adjusted to optimize the ability to detect OSA risk cases (by maximizing sensitivity) or the ability to reduce false positives (by maximizing specificity), offering a more flexible and accurate approach in the diagnosis of OSA. In the comparative evaluation between conventional BQ and the classifier based on its simplified version, the results indicate that ML-2, despite the significant reduction in the number of questions to only two, slightly outperforms BQ in terms of sensitivity and specificity (fixing one of the two variables at the BQ value). Additionally, the use of ML-2 offers the flexibility needed to adjust the operating point on the ROC curve depending on the specific needs of clinical or screening applications, thus providing a potential advantage in terms of customizing the diagnostic approach. After assessing the ML-10 and ML-2 performance against traditional BQ using a single cutoff, we expanded our analysis to include two clinically relevant AHI cutoffs. This step involved utilizing two AHI thresholds (\(\text{AHI}\geq 15\), and \(\text{AHI}\geq 30\)) commonly used in the literature to categorize the OSA severity as moderate to severe, and severe, respectively [1]. The decision to employ these specific AHI thresholds is rooted in their widespread acceptance and use in clinical practice and research for defining the severity of OSA. Such a differentiated approach allows for a more detailed assessment of the models' performance, providing insights into their predictive capabilities across a spectrum of OSA severity. This is particularly relevant for clinicians and healthcare providers seeking to tailor interventions and management strategies based on the severity of the condition. By choosing the optimal threshold for maximum accuracy, the ML-10 model performance consistently demonstrated its strength at both AHI thresholds. These results highlight the potential for a more streamlined and efficient screening process. By examining whether a simplified model can retain or surpass the full BQ predictive accuracy, this study suggests the possibility of more accessible and less cumbersome OSA screening approaches. This is especially pertinent in primary care environments or areas with limited access to specialized sleep medicine services, where a rapid and dependable screening tool could significantly improve the early detection of individuals at risk of OSA. However, we should consider that using only two questions likely makes the test sensitive but not specific, as various diseases could present with the same broad symptoms. The present study is subject to several limitations that merit consideration. Firstly, the participant cohort was drawn exclusively from two hospitals in Italy, limiting the data set representativeness of the broader population. Consequently, the predictive model developed herein might not possess widespread generalizability, potentially limiting its applicability to populations beyond the initial study setting or to diverse ethnic groups [24; 35]. Secondly, this observational study did not account for undiagnosed medical conditions commonly associated with OSA, such as neurological, cardiovascular, and pulmonary disorders. The absence of these variables could impact the model's predictive accuracy. Furthermore, our model lacked detailed anthropometric imaging or measurements, which might have restricted its ability to identify disease-specific causes of OSA accurately. In light of these limitations, there is a clear need for further research to enhance the model's robustness and applicability. To this end, we are planning a prospective clinical trial aimed at evaluating ML-10 and ML-2 across a more representative sample of the general population. This forthcoming trial is expected to address the current study limitations by incorporating a broader range of demographic and clinical variables, thereby improving the model's predictive performance and generalizability.