## 4 Discussion
In men, the excess of adipose tissue associated with OSA results in more severe OSA [40] than in women with a similar BMI and waist circumference [41]. Due to hormonal changes, women experience more severe OSA in the postmenopausal period without hormone substitution [42], whereas premenopausal women are significantly less likely to develop OSA compared to men [43]. Men are not predisposed to developing OSA due to a higher neck circumference, average body weight and free fat mass [44]. Increasing life expectancy at birth results in a greater incidence of age-related diseases. Studies over the past 10 years have shown that age and ageing and OSA induce similar physiological processes [45]. OSA is also referred to as an age-related sleep disorder based on its prevalence, as its prevalence in people over 65 years is twice that of people aged 30 to 65 years, thus increasing the number of untreated patients [46]. The above-mentioned risk factors for OSA, such as BMI, gender and age, are parameters that can be tested in everyday patient care simply and easily. In our patient population, male patients with mild and moderately severe-severe OSA were the study population. The majority of patients were over 40 years of age and their nutritional status could be characterized as overweight or obese with a BMI of 25-30 kg/m\({}^{2}\) or over 30 kg/m\({}^{2}\). In other words, the presence of all the three risk factors was observed in the OSA group. In our study, AI OSA prediction for evaluation of these parameters yielded an 81% accuracy of OSA/non-OSA classification. With AI, the presence of OSA was predicted with a 81.25% success rate, while the success rate for predicting the patient not suffering from OSA was 80.55%. If the OSA group was further differentiated along the same parameters into severity categories, the rate of success fell to 64%. The best prediction was observed for the non-OSA group, at 75%. The prediction for mild OSA proved correct for 56.25% of cases, while moderately severe-severe OSA predisposition was correct in 59.37% of the cases. To summarize, the explanation of the above findings, namely, that the success rate for OSA prediction falls with the increase of the severity of OSA, can be traced back to a number of factors. One factor could be the low number of samples. Another factor may be the deviation in age and BMI between the groups. Although the mild and the moderately severe-severe OSA subgroups consisted of mostly overweight and obese patients, the control group overwhelmingly consisted of patients of normal body weight. A significant difference in BMI could be observed between the control and the mild OSA severity subgroups, and the control and the moderately severe-severe OSA subgroups. A similar correlation could be proven for age; a significant difference being observable only between the control group and the mild OSA subgroup. Considering that in the operation of the algorithm both age and BMI played an essential role, the lack of significance between the mild and the moderately severe-severe OSA subgroups could lead to the algorithm being unable to differentiate these two subgroups. Based on the above facts. it can be concluded that the above factors could lead to the false categorization of the mild and the moderately severe-severe OSA subgroups. Self-completion questionnaires are an integral part of everyday diagnostics, with the advantage of being cost-effective and able to provide reliable results in a matter of minutes in most cases. They can be used effectively for screening in many areas of diagnostics, including sleep medicine, as well as specifically for OSA screening. The importance of questionnaires lies in the screening of previously undiagnosed OSA patients and in easing the burden on sleep laboratories. The Berlin questionnaire is one of these tools for OSA screening.