## 1 Introduction
In addition, by incorporating body mass index (BMI), age (A), neck circumference (N), and gender (G) into the STOP questionnaire, sensitivities were increased to 83.6%, 92.9%, and 100%, respectively, with the same AHI cutoffs. [PERSON] et al. [25] developed a screening tool for predicting sleep apnea based on the reported frequency of various sleep apnea symptoms and other sleep disorders, plus age, BMI, and gender. [PERSON] et al. [25] developed multiple logistic regression and the area under the receiver operating characteristic curve (AUROC) was 0.79 (\(p<\) 0.0001). [PERSON] et al. [23] validated No-Apnea with three cutoffs (AHI 5, \(>\)15, and \(>\)30), and the rates of the subjects were correctly screened as 78.1%, 68.8%, and 54.4% for AHI-5, AHI-15, and AHI-30, respectively. Overall, the developed questionnaires showed marginal accuracy in detecting the severity of OSA in multiple patients. Recent research highlights the importance and efficacy of various questionnaire-based screening tools for OSA across diverse patient populations and conditions. [PERSON] et al. (2020) combined nocturnal pulse oximetry with questionnaire-based screening, showcasing a hybrid approach for enhanced accuracy [26]. [PERSON] et al. (2021) emphasized the value of screening questionnaires in patients with atrial fibrillation, underscoring their utility in cardiovascular contexts [27]. [PERSON] et al. (2021) explored the predictive value of these tools in patients with type 2 diabetes mellitus, suggesting significant clinical relevance [28]. [PERSON] et al. (2021) demonstrated the diagnostic performance of the STOP-BANG questionnaire across different ethnic groups, highlighting its broad applicability [29]. [PERSON] et al. (2021) compared six assessment tools for OSA in hypertensive patients, providing insights into tool-specific efficacies [30]. [PERSON] et al. (2022) conducted a meta-analysis validating the STOP-BANG questionnaire as a preoperative screening tool, reinforcing its preoperative utility [31]. [PERSON] et al. (2022) provided a systematic review and meta-analysis of the diagnostic accuracy of various questionnaires in different clinical cohorts, offering comprehensive evidence of their effectiveness [32]. [PERSON] et al. (2020) addressed sex-specific performance gaps, particularly in women, indicating a need for tailored screening approaches [33]. Questionnaire-based tools for OSA screening are accessible, cost-effective, and non-invasive, facilitating early intervention, but they may lack the accuracy of polysomnography and exhibit limited sensitivity and specificity, particularly across diverse and sex-specific populations. OSA predictions have also been attempted using artificial neural networks and deep learning. [PERSON] et al. (1999) explored the use of a generalized regression neural network (GRNN) to classify OSA patients based on 23 clinical variables [34]. The model achieved a high sensitivity of 98.9% and a specificity of 80%, demonstrating the potential of ANN models in accurately diagnosing OSA from clinical data. [PERSON] et al. (2018) presented an automatic system for OSA event detection using a Convolutional Neural Network (CNN) trained on multi-modal physiological signals [35]. The model achieved an average accuracy of 79.61% across normal, hypopnea, and apnea classes. [PERSON] et al. (2016) developed an ANN model using four simple clinical inputs, sex, age, BMI, and snoring status, to diagnose OSA [36]. The model achieved an accuracy of 86.6%, indicating that ANNs can effectively reduce the need for expensive polysomnography (PSG). [PERSON] et al. (2019) applied deep learning techniques to analyze ECG and SpO\({}_{2}\) signals for OSA detection [37]. The model demonstrated good performance, but the study highlighted challenges in generalizing the results due to the limited sample size and the need for extensive manual feature extraction. This study addresses the need for a more efficient and accurate method to predict severe OSA.