## 4 Experimental Validation  
### Performance Evaluation  
We use the following metrics for the evaluation of the performance, which were calculated according to Ref. [82]: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline initial learning rate & \(1\times 10^{3}\) \\ min learning rate & \(1\times 10^{7}\) \\ scheduler & cosine annealing with warm \\  & restarts—200 epochs \\  & batch size & 8 \\  & optimizer & AdamW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyper-parameters of the U-lossian network. Figure 3: Architecture of the U-lossian network classifier. Sensitivity or true positive rate (TPR) is the probability that a predicted result will be positive when the disease is present. \[Sensitivity=a/(a+b) \tag{4}\]  
Specificity or true negative rate (TNR) is the probability that a predicted result will be negative when the disease is not present. \[Specificity=d/(c+d) \tag{5}\]  
where \(a\) represents true positives (TP), participants who have the disease and positive test findings; \(d\) represents subjects who do not have the disease and the test agrees (TN); \(b\) represents individuals who do not have the disease but the test shows 'disease' (FP); and \(c\) represents false negatives (FN). The positive likelihood ratio (PLR) is the ratio of the likelihood of a positive test result given the existence of the illness to the likelihood of a positive test result given the absence of the disease, i.e.,  
\[PLR=TPR/FPR=Sensitivity/(1-Specificity) \tag{6}\]  
The negative likelihood ratio (NLR) is the ratio of the likelihood of a negative test result in the presence of disease to the likelihood of a negative test result in the absence of disease, i.e.,  
\[NLR=FNR/TNR=(1-Sensitivity)/Specificity \tag{7}\]  
When screening people, predictive values are more important than sensitivity and specificity. The positive predictive value (PPV) is the probability that the disease is present when the prediction is positive. \[PPV=a/(a+b) \tag{8}\]  
The negative predictive value (NPV) is the probability that the disease is not present when the prediction is negative. \[NPV=d/(c+d) \tag{9}\]  
Accuracy is the overall probability that a voice sample is correctly classified. \[Accuracy=Sensitivity\times Prevalence+Specificity\times(1--Prevalence) \tag{10}\]  
The misclassification rate (MCR) is a measure that indicates the ratio of outcomes predicted incorrectly by a classification model. \[MCR=(b+c)/(a+b+c+d) \tag{11}\]  
[PERSON] presented an index to assess the quality of a screening test. The index is defined using the false positive and false negative values as  
Figure 3:0  
The false discovery rate (FDR) is the predicted proportion of incorrect positive classifications to total positive classifications (rejections of the negative class). Figure 3:1  
The false omission rate (FOR) measures the ratio of false negatives that are incorrectly rejected. Figure 3:2Balanced accuracy (BA) is the average of the recall scores acquired for each class, that is, the general average of the recall scores per class. Figure 3:3  
The F1-Measure is the harmonic mean of the precision and recall. Figure 3:4  
The G-measure (or Fowlkes-Mallows index) is a metric for measuring the confusion matrices that is used to quantify the similarity between two clusterings (clusters formed following a clustering technique), where each cluster represents a different class. Figure 3:5  
Matthews index is a balanced measure that yields a number between 1 and +1 as a correlation coefficient between predicted and true binary classifications. A coefficient of +1 implies a flawless forecast, a coefficient of 0 shows no better than random prediction, and a coefficient of 1 indicates absolute disagreement between prediction and observation. Figure 3:6  
The critical success index (CSI) is a categorical prediction performance verification metric equal to the total number of correct predictions divided by the total number of predictions plus the number of misses.