## 3 Results
### Three-Group Patient Categorization with the Berlin and Epworth Questionnaire
AI Prediction of OSA Categorization Based on Basic Anthropometric Parameters and the Berlin and Epworth Questionnaires with Patients Categorized into Mild OSA, Moderately Severe-Severe OSA and Non-OSA  
Based on the favorable findings with the two-group categorization, the investigation of the applicability of AI for more sophisticated categorization was continued. The results of AI prediction of OSA categories using age, gender, BMI, and Berlin and Epworth questionnaire responses are presented in Table 12.  
Based on the above described data, the accuracy was 69%. The hit rate in the case of non-OSA categories was 80.55%, in the case of the mild OSA category it was 59.37%, and the moderately severe and severe OSA cases could be determined with a 65.62% accuracy. Classification of Patients into Three Groups Based on Demographic, Anthropometric and Questionnaire Information  
In theory, an increased amount of information reduces categorization error. Therefore, in the final phase of research, the predictability of OSA based on all relevant information was tested. The results of the categorization by age, gender, BMI, neck circumference and data from the Berlin and Epworth questionnaires are presented in Table 13. \begin{table}
\begin{tabular}{c c c} \hline \hline
**OSA Estimation Based on the** & & \\
**Result of Berlin and Epworth** & & \\
**Questionnaires and BMI**, & & \\
**Age, Gender and** & & \\
**Neck Circumference** & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Non-OSA (n = 36)** & **Mild OSA (n = 32)** & **Moderately Severe-Severe OSA (n = 32)** \\ \hline Non-OSA & 29 (80.55\%) & 6 (18.75\%) & 2 (6.25\%) \\ \hline Mild OSA & 6 (16.66\%) & 21 (65.62\%) & 9 (28.13\%) \\ \hline Moderately severe–severe OSA & 1 (2.79\%) & 5 (15.63\%) & 21 (65.62\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: AI prediction of OSA based on age, gender, BMI, neck circumference and data from 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 c} \hline \hline
**OSA Estimation Based on the** & & & \\
**Result of Berlin and Epworth** & & & \\
**Questionnaire and BMI, Age** & & & \\
**and Gender** & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**OSA Estimation Based on the** & & & \\
**Result of Berlin and Epworth** & & & \\
**Questionnaire and BMI, Age** & & & \\
**and Gender** & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Non-OSA (n = 36)** & **Mild OSA (n = 32)** & **Moderately Severe–Severe OSA (n = 32)** \\ \hline Non-OSA & 29 (80.55\%) & 7 (21.88\%) & 4 (12.50\%) \\ \hline Mild OSA & 5 (13.90\%) & 19 (59.37\%) & 7 (21.88\%) \\ \hline Moderately severe–severe OSA & 2 (5.55\%) & 6 (18.75\%) & 21 (65.62\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: AI prediction of OSA based on age, gender, BMI, and 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. By using all the information available: the results of the Berlin and Epworth questionnaires, BMI, age, gender and neck circumference, 71% confidence in categorization was achieved. A relatively high hit rate (80.55%, 29 out of 36 patients) was obtained in the non-OSA category and a lower rate (65.62%) in the mild OSA group, similar to the moderately severe-severe OSA group.