## 4 Discussion  
The factors involved in the pathophysiology of OSA can be divided into two main groups. One consists of anatomical factors. Anatomically narrow and prone to collapse, upper airways are parameters that can be measured by diagnostic imaging studies and are responsible for two-thirds of severe OSA [26]. The other main group is represented by non-anatomical factors, which include unstable airway control (high loop gain) [26,27], low arousal threshold [28] and reduced upper airway dilator muscle activity [29]. These factors can be measured by PSG examination. OSA severity is determined by sleep stages, which can also be assessed by PSG examination [30]. It is evidenced that PSG plays a key role in the diagnosis of OSA, both in terms of as-sessing the presence of the disease and its severity. Knowing the severity of OSA is essential for choosing a suitable treatment plan. Basically, in cases of mild OSA, surgical therapy while in mild-severe and severe cases of OSA continuous positive airway pressure therapy is the primary treatment of choice. With the timely diagnosis and treatment of OSA, comorbidities can be prevented [31,32]. However, the rapidly increasing number of patients and poor access to health care, as well as the urgent need to reduce the burden on resources, have led to the introduction of home sleep tests and the development of screening questionnaires for preliminary OSA diagnosis. These, while not providing an accurate diagnosis, can indicate the possible presence of the disease. Risk factors for OSA, such as central obesity, male gender or age over 40-50 years, play a crucial role in the development of OSA. The questionnaires aim to assess the symptoms of OSA and their extent, in addition to these predisposing factors. The questionnaires are effective in screening for OSA because their relatively high sensitivity and high negative predictive value are effective in screening for OSA, but they do not provide information on the severity of the disease. From basic anthropometric parameters, demographic data and questionnaire results, a large database can be constructed, which lends itself to be a reliable basis for the prediction of OSA with AI. Based on the above findings, our research investigated the predisposing factors for OSA according to the traditionally used Berlin and Epworth questionnaires, as well as the success of predicting and categorizing OSA using AI. The aim of our research was to de-velop a simple, practical and reliable prediction system. Our results demonstrated that the database, even with minimal data obtained from a relatively small number of patients can be effectively applied in the preliminary diagnosis of OSA. This reduces the number of untreated patients and contributes to the enhancement of efficiency of material and human resources. The most important risk factor for OSA is obesity, which is measured using BMI. It is well established that a higher BMI significantly increases the risk of developing OSA compared to individuals with a normal BMI, and this risk is even more pronounced for a BMI above 35 kg/m\({}^{2}\)[33]. Consequently, changes in body weight affect the severity of OSA. A 10% increase in body weight leads to a 32% increase in AHI [34], while weight loss leads to an improvement in OSA symptoms and a decrease in AHI [35]. The disadvantage of using BMI this way is that it does not produce an accurate indication of real body fat percentage [36], with overestimated BMI for high muscle mass and underestimated values for high body fat percentage with normal BMI [37]. Male gender is also an important risk factor for OSA, the effect of which may be partly explained by hormonal differences between men and women [38], which may also affect the distribution of body fat. The gender difference in adipose tissue is reflected in an increase in visceral adipose tissue in men and global subcutaneous adipose tissue in women [39].