## 2 Participants and Methods
### Machine Learning Predictive Value  
Calculating group statistics is crucial in establishing the statistical relevance of variables within a diagnostic context, allowing for the assessment of risk factors and relationships with comorbidities. However, it is widely recognized that statistical relevance does not equate to discriminant power, which is more critical for classification and prediction tasks. Variables that are statistically significant in a model do not necessarily guarantee superior prediction performance, and attributes deemed non-significant might be predictive. Therefore, we opted to investigate the predictive capabilities of the \(\mathrm{BQ}\) using ML techniques. To this end, six distinct classifiers were evaluated for their suitability in the predictive task: Naive Bayes, Support Vector Machine (SVM), Decision Trees, Error-correcting Output Codes (ECOCs), Discriminant Analysis, Ensemble of decision trees, and Artificial Neural Networks (ANNs). Among these, the Ensemble of decision trees demonstrated the best performance. This model was initially trained with the ten responses from the standard \(\mathrm{BQ}\) and then separately with only the two responses from the simplified version, \(\mathrm{BQ}\)-\(\mathrm{2}\), independently, resulting in the development and evaluation of two distinct models designated as ML-\(\mathrm{10}\) and ML-\(\mathrm{2}\), respectively.  
We employed a 10-fold cross-validation (CV) approach for the training and quality assessment. For both models, features were normalized to a 0-1 range using min-max normalization on the training dataset in each CV iteration, with identical normalization parameters applied to the corresponding validation set.  
The Receiver Operating Characteristic (ROC) curve was used to illustrate the diagnostic capability of the models at various decision thresholds, providing a graphical representation of the trade-off between sensitivity (true positive rate) and 1-specificity (false positive rate). Initially, we identified the specific operating point in the ROC space corresponding to the conventional \(\mathrm{BQ}\), indicating the combined sensitivity (ability to correctly identify cases at high risk of OSA) and specificity (ability to correctly identify low or non-OSA cases) achieved without integrating ML techniques. Subsequently, we compared this point with the performance of the ML-enhanced models (both ML-\(\mathrm{10}\) and ML-\(\mathrm{2}\)) at equal specificity and equal sensitivity, by vertically and horizontally adjusting them from the \(\mathrm{BQ}\) point until the ROC curve of the ML-\(\mathrm{10}\) model was intersected. This approach allowed us to evaluate how ML-\(\mathrm{10}\) and ML-\(\mathrm{2}\) could enhance sensitivity while maintaining the specificity of the conventional \(\mathrm{BQ}\), and vice versa.  
Subsequently, we extended our analysis to evaluate the ML-\(\mathrm{10}\) and ML-\(\mathrm{2}\) models across two different AHI thresholds (\(\mathrm{AHI}\geq\mathrm{15}\), and \(\mathrm{AHI}\geq\mathrm{30}\)) referenced in the literature to classify OSA as moderate to severe, or severe, respectively [1]. For this purpose, the ROC curve was utilized to assess the classifier performance and to determine an \"optimal\" prediction threshold that maximizes accuracy. Binary classifiers were derived from this optimal operating point. Performance metrics including the Area Under the Curve (AUC), accuracy, sensitivity, and specificity were used to measure the models' effectiveness. All computational analyses were performed using MATLAB software, version R2023b.