## 4 Discussion
When the two questionnaires (Berlin and Epworth) were used together, the success rate of prediction by severity improved to 63%, and the prediction of mild and moderately severe-severe OSA was successful in 53% of cases. In other words, when using the two questionnaires with AI, it was non-OSA prediction that could be improved significantly (80%), while the prediction of mild and moderately severe-severe OSA showed a result similar to using the Berlin questionnaire on its own. Using the two questionnaires together, [PERSON] et al. found the specificity to be 0.72 at an AHI cut-off of 5 [51]. Overall, it can be observed that the effectiveness of both questionnaires was greatly improved by the use of AI. For resource optimization, the benefits of AI should be progressively exploited in the diagnosis of OSA. Given the available clinical data, AI can be used to screen high-risk patients [52]. [PERSON] et al. found a 0.53 correlation coefficient between AI prediction of OSA, based on sleep questionnaire results and OSA based on RDI [53]. AI prediction of OSA by questionnaires was found to have 88% sensitivity and 97% specificity for AHI above 15 [13]. In our study, prediction of 15 < AHI was found to be most effective when age, gender and BMI were added to the Berlin and Epworth questionnaires. The information provided by neck circumference contributed only marginally to the prediction of OSA severity. The improvement seen with neck circumference in AI augmented OSA prediction may be explained by the significant difference between the study groups. The efficiency of OSA prediction can be vastly improved if parameters that show significant differences between groups are used. The prediction of OSA and its severity showed different results for electrocardiogram, demographic and 26-parameter PSG using vector machine, k-nearest neighbor and naive Bayes algorithms. Prediction of OSA based on clinical characteristics (AHI < 5 and AHI > 5) by vector machine analysis showed 59% sensitivity and 74.5% specificity, where-as the prediction based on electrocardiogram parameters showed 43.4% sensitivity and 83.5% specificity. Predictions based on these parameters, using a naive Bayesian algorithm, showed a sensitivity of 57.5% and a specificity of 73.7% for predictions based on clinical parameters, while electrocardiogram-based predictions showed a sensitivity of 39% and a specificity of 82.7%. Further analysis confirmed a moderate correlation between the AHI and BMI, arousal index and electrocardiogram parameters [54]. These results are in line with our estimations: no significant stochastic relationship could be found between AHI and any anthropometric parameters. Conversely, the BMI has shown significant correlation with all anthropometric parameters. [PERSON] et al. [55] have applied neural network modelling for the diagnosis of OSA based on the clinical characteristics of 405 patients. The cut-off criterion was AHI > 10. The sensitivity of the classification was 99%, while its specificity was as high as 80%. Using the same cut-off criterion and a logistic model, [PERSON] et al. categorized the 370 patients in their sample by 76-96% sensitivity and 13-54% specificity [56]. The above citations highlight that our results are in line with the results of other academic workshops. Overall, it can be concluded that the application of AI in itself for OSA prediction by age, gender and BMI significantly improves the identification of high-risk OSA patients. The analysis of questionnaire-based results using AI offers further possibilities. The addition of demographic and anthropometric parameters to the data supplied by the patients responding to the questionnaires will aid their categorization. It is important to note, however, that results categorized for OSA severity exceed 50% in many cases, but this comes with limited clinical consequences. The relevance of the categorization lies in the exclusion of non-OSA cases, which, many times, proved as high as 80%.