## 3 Results
### Performance Results  
The proposed classification algorithm yielded a reliable computational result, as shown in Table 1, with five performance parameters, such as the area under the receiver operating characteristic (ROC), Sn, Sp, Pr, and Ac. The optimal performance result based on the threshold of 0.875 shows an area under ROC (AUC) of 94.7%, an Sn of 94.74%, an Sp of 75%, a Pr of 94.74%, and an Ac of 91.30%. The threshold determined the empirical balance between sensitivity and specificity. The AUC, as well as common performance metrics (i.e., Sn, Sp, Pr, and Ac), is known to reflect the performance of the algorithm [45]. The ROC curve and the AUC of the DNN model were compared to the other classifiers, including random forest (RF) [46], AdaBoost (ADB) [47], K-neighbors (KNs) [48], support vector machine classifier (SVMC) [49], and decision tree (DT) [50], as shown in Figure 4 and Table 2. The proposed DNN/scaled PCA algorithm was optimal, followed by the SVMC, and the top two best results are highlighted in bold in Table 2. In order to further analyze the results in more detail, we plot the confusion matrices as shown in Tables 3 and 4, indicating  
in this study included two hidden layers, each with 120 neurons, 500 training epochs, a patience value of 18, and a batch size of 2. The DNN integrated with a scaled PCA classifier was employed to predict severe apnea. However, the correlation coefficients between various patient parameters and severe apnea revealed unclear relationships. Table 5 lists coefficients with absolute values exceeding 0.36. The highest correlation coefficient (+0.68) is highlighted in bold, while informative parameters with coefficients over an absolute value of 0.52 are italicized and shown in red. Our methodology utilizes indirect or limited data, such as demographic information and responses to existing questionnaires, to predict severe sleep apnea. This approach aims to reduce future medical costs and streamline the diagnostic process. Despite its advantages, this study has several limitations, including the lack of input data separation and the absence of longitudinal data. The reliance on survey data presents challenges due to its discrete format. Although scaled PCA was employed to enhance data resolution, incorporating additional input variables would be beneficial. Furthermore, the applicability of our results is confined to subjects with severe sleep apnea. This specific focus may affect the overall accuracy of the predictive model when applied to a broader population. Therefore, while our method shows promise in identifying severe sleep apnea, further research is necessary to improve its generalizability and accuracy for the wider population. The hyperparameter optimization process involved testing various configurations to determine the most effective combination. Real-valued hyperparameters, such as the learning rate, were adjusted to find an optimal balance between convergence speed and stability. Integer-valued parameters, including the number of layers and neurons, were fine-tuned to ensure the network's capacity was sufficient to capture complex patterns without overfitting. Binary parameters, like early stopping, were evaluated to prevent unnecessary training and reduce overfitting. Finally, categorical parameters, such as the choice of optimizer, were selected based on their performance in training the model effectively. In predicting severe sleep apnea, the scaled PCA classifier played a crucial role in transforming the input data into a format that the DNN could process efficiently. This transformation helped in managing the data's dimensionality and enhancing the model's predictive capabilities. Despite the unclear relationships indicated by the correlation coefficients, the identification of key parameters with significant correlations provides valuable insights for further model refinement. The use of demographic information and questionnaire responses underscores the potential of non-invasive and cost-effective methods in medical diagnostics.