## 3 Results
### Three-Group Patient Categorization with the Berlin and Epworth Questionnaire  
Filling out the Berlin and Epworth questionnaires takes very little time. Hence, in clinical practice, it seems reasonable to apply both questionnaires. The applicability of data obtained by joint utilization of both questionnaires was tested for OSA prediction. Results are summarized in Table 10.  
Based on the results of the Berlin and Epworth questionnaires, 63% of the categorizations were correct using AI. For the non-OSA group, 80.55% of the predictions proved correct. This ratio was 53% for the mild OSA group, and 53.12% for the moderately severe-severe OSA categories. AI Prediction of OSA Based on Demographic and Anthropometric Parameters Combined with Data from the Berlin and Epworth Questionnaire  
In the last phase of research, the joint applicability of data was tested. Data were obtained by collecting demographic and anthropometric parameters and responses to the items of the Berlin and Epworth questionnaires. The results for a two category AI classification based on the age, gender, BMI and the responses to items in the Berlin and Epworth questionnaires are presented in Table 11. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**OSA Estimation Based on the Results of the Berlin and Epworth Questionnaire**} & \multicolumn{3}{c}{**Real OSA Categorization**} \\ \cline{2-4}  & & & **Moderately** \\
**Eworth Questionnaire** & **Non-OSA (n = 36)** & **Mild OSA (n = 32)** & **Severe–Severe** \\  & & & **OSA (n = 32)** \\ \hline Non-OSA & 29 (80.55\%) & 11 (34.38\%) & 5 (15.63\%) \\ \hline Mild OSA & 5 (13.89\%) & 17 (53.12) & 10 (31.25\%) \\ \hline Moderately severe–severe OSA & 2 (5.56\%) & 4 (12.5\%) & 17 (53.12\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: OSA prediction based on the joint results of the Berlin and Epworth questionnaires (the table shows the number of cases per 100 patients and the percentage distribution of scores within OSA categories). OSA: obstructive sleep apnoea. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**OSA Estimation Based on the Result of the** **Berlin and Epworth Questionnaire**} & \multicolumn{3}{c}{**Real OSA Categorization**} \\ \cline{2-3}  & **Non-OSA (n = 36)** & **Mild OSA (n = 32)** & **Moderately** \\
**Eworth Questionnaire** & **Non-OSA (n = 36)** & **OSA (n = 64)** \\ \hline Non-OSA & 28 (77.78\%) & 9 (14.07\%) \\ \hline OSA & 8 (22.22\%) & 55 (85.93\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: AI prediction of OSA based on age, gender, BMI and responses to the Berlin and Epworth questionnaires (the table shows the number of cases per 100 patients and the percentage distribution of scores within the non-OSA and OSA categories). OSA: obstructive sleep apnoea. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**OSA Estimation Based on the Result of the** **Eworth Questionnaire**} & \multicolumn{3}{c}{**Real OSA Categorization**} \\ \cline{2-3}  & **Non-OSA (n = 36)** & **Mild OSA (n = 32)** & **Moderately** \\
**Eworth Questionnaire** & **Non-OSA (n = 36)** & **OSA (n = 32)** & **Severe–Severe** \\  & & & **OSA (n = 32)** \\ \hline Non-OSA & 19 (52.77\%) & 1 (3.20\%) & 11 (34.37\%) \\ \hline Mild OSA & 13 (36.11\%) & 31 (96.80\%) & 15 (46.88\%) \\ \hline Moderately severe–severe OSA & 4 (11.12\%) & 0 & 6 (18.75\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: OSA category prediction based on the result of the Epworth questionnaire (the table shows the number of cases per 100 patients and the percentage distribution of scores within OSA categories). OSA: obstructive sleep apnoea. The accuracy of prediction was 83%. Categorization of non-OSA patients was correct in 28 out of 36 cases (77.78%), while OSA categorization was correct in 55 cases out of 64. This means an 85.93% accuracy.