## 1 Introduction
The prediction models described in existing research primarily utilize clinical data, such as demographic information (age and gender), comorbid conditions, anthropometric measures (Body Mass Index (BMI), waist and neck circumferences), symptoms of OSA, and physiological parameters (blood pressure, overnight pulse oximetry, and lung function tests). The effectiveness of these models in predicting OSA, as indicated by an AHI \(\geq\) 5/h, has shown sensitivity rates ranging from 66% to 100% and specificity rates ranging from 30.8% to 76.2%. For predicting more severe OSA (AHI \(\geq\) 15/h), the sensitivity ranges from 60.3% to 92.7%, with the specificity ranging between 33.3% and 90.7% [24]. The variability in these models' ability to discriminatebetween cases may be due to factors such as the complexity of the models, sample size, OSA prevalence, and the proportion of cases with different severities of OSA. It is noted that most OSA prediction models prioritize higher sensitivity over specificity to facilitate early diagnosis, although this approach may result in a higher rate of false positives and potentially lead to unnecessary PSG testing [24]. The Berlin questionnaire (BQ) [28] stands out as one of the simplest and most widely implemented non-invasive screening tools for diagnosing OSA, demonstrating a sensitivity of 86% and a specificity of 95% for OSA diagnosis. Originally introduced in the United States (US), the BQ consists of a concise set of questions focused on the risk factors and symptoms associated with OSA, aimed at identifying patients at high risk who might benefit from undergoing PSG to facilitate increased diagnosis rates. While the standard BQ comprises 10 questions, we previously introduced a streamlined questionnaire version by using a trained classifier [22], reducing the questionnaire to just two questions (\"simplified Berlin questionnaire\", or BQ-2). This abbreviated version has been shown to achieve results comparable to the original BQ, offering an efficient means of rapidly screening high-risk OSA patients. The main aim of this research was to enhance the sensitivity, specificity, and accuracy of the conventional BQ by incorporating ML techniques. For this purpose, we developed an ML-enhanced BQ model (ML-10) capable of predicting the risk of OSA using the BQ items as model features. Additionally, we explored a simplified version of ML-10, called ML-2, based on BQ-2 [22], to determine whether it yields comparable results. The predictive performance of these models was evaluated against the conventional BQ approach, which does not incorporate ML techniques. Furthermore, we utilized the ML-10 and the ML-2 models to identify patients with OSA at two different AHI thresholds: \(\geq\)15/h, and \(\geq\)30/h, thereby assessing their efficacy across a spectrum of OSA severity. In conclusion, the integration of an ML algorithm into the conventional BQ demonstrated a significant enhancement in the ability to predict the risk of OSA across various severity thresholds. This advancement underscores the potential of ML-enhanced diagnostic tools in improving the early detection of OSA. The findings of this research validate the application of innovative ML approaches in enhancing the diagnostic processes for OSA, potentially leading to more timely and effective interventions for this widely prevalent but underdiagnosed condition. The remaining sections of this paper are organized as follows: Section 2 details the participants and methods used in this study, including the study design, OSA diagnosis process, and ML predictive models. Section 3 presents the results of our experiments, comparing the performance of the conventional BQ, the ML-10 model, and the simplified ML-2 model. Section 4 discusses the implications of our findings, situates our work within the broader context of existing research, and outlines the limitations of our study. Finally, Section 5 concludes the paper with a summary of our contributions and suggestions for future research.