## 1 Introduction  
Obstructive sleep apnoea (OSA), which is a disease affecting ca. 1 billion people in the age bracket of 30 to 60 worldwide, comes with a complete or partial obstruction of the upper airways during sleep [1]. Despite its increasing prevalence and the progressive expansion of diagnostic options, it is underdiagnosed [2]. Left untreated, it can lead to a high incidence of co-morbid conditions (myocardial infarction [3], stroke [4], hypertension [3], diabetes mellitus [5], cardiac arrhythmias [3], and depression [6]) due to pathophysiological processes activated by hypoxia and hypercapnia caused by recurring episodes of upper airway obstruction during sleep [7]. The gold standard of OSA diagnosis has been polysomnography (PSG) in sleep laboratories [8], but its human and material conditions, cost and its relatively constrained accessibility prevents the proliferation of sleep laboratories according to demand. This gave rise to an acute need to expand the arsenal of OSA diagnostics with various screening methods and rapid diagnostic tools for pre-screening patients, and to reduce the burden on sleep laboratories. In recent years, there has been an undoubtedly significant development in the instrumental diagnosis of OSA owing to the proliferation of portable device-based home sleep tests in some developed countries. Portable devices and home sleep tests have many ad-vantages. They can be performed at home, in an environment more comfortable for the patient; they require no presence of technical staff and much less equipment. They are, however, not yet widely available in less affluent countries, so simpler methods for pre-screening patients still play a significant role. Targeted sleep questionnaires provide further opportunities as part of routine outpatient care in everyday clinical practice. The advantage of questionnaires is that they can provide an overview of OSA likelihood in a matter of minutes. A number of questionnaires can be used to screen for OSA. The most widely used are the Epworth, Berlin, STOP, and STOP-BANG questionnaires [9]. Artificial intelligence (AI) is the field of science that applies mathematical statistics and computer science to analyze complex databases. Its benefits are useful for diagnostics, in therapeutic outcomes and for predicting the possible presence of disease. The history of AI and modern computing dates back to 1950 when the British mathematician [PERSON] began applying neural networks to solve classification problems. He coined the term 'Turing test' and investigated the extent to which computers could be used to make logical judgements, i.e., decisions [10]. The applicability of AI in medicine has been the subject of much research since [PERSON]'s work in the middle of the last century. Since the last two decades, there have been significant advances in medicine and a growing demand for the wider use of AI [11]. With the emergence of a large number of sleep studies and the dynamic growth of the discipline, sleep medicine has laid the foundations for advancing the application of AI. Some recent results are based on big data databases, others on cloud-based information storage and decision support processes [12]. One of the main goals of sleep diagnostics is to start treating patients as soon as possible and prevent co-morbidities through a timely and cost-effective diagnosis. Our research aims to improve predictability by comparing alternative and cost-effective diagnostic options for OSA diagnosis using AI. The application of AI for the prediction of OSA has a history more than a decade long [13]. Our article is a continuation of this.