## 5 Discussion  
Following the analysis of our model results, we conducted an in-depth evaluation of the correlations between obstructive sleep apnea (OSA) and various physiological and demographic factors, as depicted in Figure 2.  
Our statistical analysis unveiled several noteworthy associations with the diagnosis of OSA. The correlation matrix heatmap provides a vivid visual representation of these relationships. A significant positive correlation emerged between snoring intensity and OSA diagnosis (r = 0.38), reinforcing snoring as a predominant symptomatic predictor of OSA. Conversely, the correlation between self-reported tiredness and OSA diagnosis was found to be weak (r = 0.12), suggesting that while tiredness is a common complaint among individuals with OSA, it may not serve as a definitive indicator of the disorder. Notably, the presence of observed apnea episodes demonstrated a significant positive correlation with OSA diagnosis (r = 0.28), highlighting its importance as a critical clinical sign of OSA. Moreover, we observed a moderate positive correlation between blood pressure and OSA diagnosis (r = 0.21), indicating a potential link that might contribute to the observed comorbidity in patients with OSA. Furthermore, BMI showed a positive correlation with OSA diagnosis (r = 0.13). Although BMI is a well-established risk factor, its predictive value seems less pronounced compared to other physical indicators within our cohort. Additionally, our study identified a positive correlation between age and OSA diagnosis (r = 0.14), implying that the likelihood of being diagnosed with OSA slightly increases with age in our study population. Neck circumference also revealed a moderate positive correlation with OSA diagnosis (r = 0.23), underscoring its relevance as a measurable risk  
Figure 1: Evaluation of optimal models. factor for OSA. Furthermore, gender exhibited a positive correlation with OSA diagnosis (r = 0.22), aligning with previous research that indicates a gender disparity in the prevalence of OSA. In the landscape of OSA diagnostics, the deployment of ML models signifies a pivotal shift towards precision medicine. Our study's comparative analysis across Logistic Regression, Random Forest, SVM, and K-NN algorithms underscores the critical role of model selection, influenced by nuanced diagnostic criteria and the inherently complex nature of OSA. Particularly, the superior performance metrics of the K-NN algorithm highlight its efficacy as an OSA screening tool. This observation necessitates further exploration into optimizing distance metrics and weighting methodologies to augment diagnostic accuracy. Additionally, the observed variability in the performance of logistic regression, contingent on the regularization parameter C, emphasizes the importance of precise model tuning. This variability highlights the need for a thorough exploration of regularization techniques to mitigate model bias and variance, especially within the intricate context of medical data relationships. The SVM model's notable sensitivity and F1 score at optimal C values suggest its adeptness in distinguishing between patients with OSA and control groups, prompting further investigation into diverse kernel functions to refine the SVM's application in OSA diagnostics and enhance its predictive precision. Although the Random Forest model exhibits consistent performance, a deeper examination of hyperparameters is warranted to achieve a balance between model complexity and robustness, facilitating its practical application in clinical settings. In line with this perspective, the objective of this study was primarily focused on enhancing specificity, which led to the highest evaluation of the K-NN model. However, it is evident that there is a need to explore the viewpoints of other models. The cross-sectional design of this study, while providing initial insights, inherently limits the capacity for causal inference.