## 4 Discussion
The advantage of this method lies in its cost-effectiveness and simplicity, which could help alleviate the bur-den on sleep laboratories. This method could also contribute to a faster diagnosis of OSA patients. There are three limitations of the current study:  
1. The relatively small number of patients in the sample. Our approach was based on the principle circumstance that there was a constant flow of patients to the Department of Otolaryngology and Head and Neck Surgery of Semmelweis University. We sampled this flow. Due to the limitation of material and human resources, taking into consideration that the research project needed the purposeful well-designed cooperation of different specialists from various branches of the medical profession, this setting was more easily operationalisable than obtaining a random sample from a large pool of patients reflecting the different characteristic features of the population of patients, aiming for the achievement of a representative sample. Obviously, the clinical usability of the present results require careful consideration since their efficiency does not match the efficiency of home sleep apnoea tests. At the same time, it can be asserted that, in the dynamically developing world of artificial intelligence, a great number of studies in many countries of the world attempt to enhance diagnostics and therapeutic efficiency, in many cases, with studies that have an even smaller number of patients in their samples [57; 58; 59; 60]. 2. Our work is a compromise of length and content. It is well documented that a high number of parameters could be additionally applied in the screening of OSA, but we wanted to show that even the simplest parameters could be used with high efficiency in the screening process. Similarly to other studies, the present paper seems to have proved that using even these simple and easily measurable parameters can offer the opportunity to effectively screen OSA even with patient numbers this low [61]. The aim of our research was to showcase a screening method for OSA that delivered relatively accurate predictions with the use of the least number of cheap parameters that were readily available in everyday diagnostics. In other words, we wanted to know whether we could predict OSA with parameters this few and this simple. This explains why we did not use those important parameters that would have reflected the severity of OSA. 3. The application of machine learning models is developing at an exponential rate, and currently we are very far from having an algorithm that could be considered \"the gold standard\" [62]. In our personal estimation, in our present day there are more than a thousand possible algorithms which could be applied to classify different objects based on a relatively small sample. Our goal was not to establish an exhaustive set of these classifiers and choose the best of them, but simply to apply some of them that are relatively easily available, choose the most efficient ones and then demonstrate the results  
Although our efforts could be presented as a preliminary result because of the small sample size, on the whole, the results demonstrate the future applicability of the method for the screening of OSA. Our further aims are the expansion of the sample size and the improvement of the algorithms for a more accurate prediction of OSA. With perfecting the algorithm, and as a result, with a more accurate diagnosis of OSA, the more widespread screening of OSA patients and the deloading of OSA screening diagnostics will become achievable in the future. As a result, it was not possible to separate more sophisticated subcategories by severity of OSA and the moderate and severe OSA categories had to be merged. The basic objective of our study was to further the methodology of OSA diagnosis; therefore, we did not aim to set up a sample, representing the Hungarian population as a whole. We accepted the distorted characteristics of the sample, e.g., a high proportion of men and relatively young patients.