## 4 Results
### MLP and RNN Accuracy, F1 Score, Loss  
Figure 5 shows the accuracy of MLP and RNN models in predicting speech audiometry from PTA data, while Figure 6 represents the loss values for each model. To evaluate the performance of the models designed in this study, the test dataset was used with evaluation metrics including accuracy, log loss, and F1 score, and a confusion matrix was utilized to analyze classification performance for each class. The MLP model achieved a test accuracy of 85.77%, correctly classifying 85.77% of the samples. The test loss was 0.3181, indicating a relatively low difference between predicted probability distributions and actual labels. The F1 score was 0.8596, demonstrating that the model effectively balanced precision and recall. Similarly, the RNN model was evaluated using the same performance metrics based  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software/Parameters** & **Value** \\ \hline Windows 10 & 64-bit \\ Programming language & Python, Keras, TensorFlow \\ CPU & Intel(R) Core (TM) 9-14900K \\ GPU & Nvidia GeForce RTX 4090 \\ RAM & 128 GB \\ Batch size & 16 \\ Validation split & 0.2 \\ Test split & 0.2 \\ Optimizer & Adam \\ Learning rate & 0.001 \\ Loss function & Huber \\ Epochs & 300 \\ Dataset & 12,972 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Software and simulation parameters.  
on the test dataset. The test accuracy of the RNN model was 85.41% and the test loss was 0.3796, representing the quantitative difference between predicted probabilities and actual labels. The F1 score was 0.8548, indicating that the model maintained a balanced classification performance across multiple classes. Both models demonstrated strong classification performance, with the MLP model achieving slightly better accuracy and lower loss than the RNN model. However, the RNN model maintained a competitive F1 score, suggesting its effectiveness in capturing sequential dependencies in the PTA dataset.  
In this study, a confusion matrix was used to analyze and compare the performance of the MLP and RNN models designed for solving the multi-class classification problem, as shown in Figure 7. Each model was evaluated based on its classification performance across three classes (0, 1, 2), allowing for an assessment of inter-class confusion and the strengths of each model. An analysis of the confusion matrix for the MLP model showed that class 0 recorded 682 true positives (TPs), with 55 false positives (FPs) (\(55+1\)), indicating that the model effectively distinguished class 0 from the others. Class 1 recorded 670 TPs with 58 FPs (\(10+58\)), which remained relatively low. However, 10 false negatives (FNs) suggested  
Figure 5: MLP and RNN model accuracy.  
Figure 6: MLP and RNN model loss.  
some confusion with class 2. Class 2 recorded 531 TPs, but its figure of 206 FNs was relatively high, revealing considerable misclassification between class 1 and class 2. This result suggests that class 2 had overlapping distributions with class 1 or lacked sufficient representative data.  
For the RNN model, class 0 achieved 692 TPs with 43 FPs (\(43+3\)), demonstrating a classification performance similar to MLP in distinguishing class 0. Class 1 recorded 656 TPs with 52 FPs (\(30+52\)), indicating a higher misclassification rate, particularly between class 1 and class 2. Class 2 achieved 543 TPs with 193 FNs (\(2+193\)), showing lower confusion than class 1. This suggests that the RNN model effectively learned the distinguishing features of class 2 due to its ability to capture temporal patterns. Overall, the MLP model demonstrated higher accuracy in classifying class 0 and class 1, performing well in cases where data distribution overlap was minimal. In contrast, the RNN model showed better classification performance for class 2, mitigating some of the class overlap issues. However, the RNN model exhibited increased FPs and FNs for class 1, suggesting a higher misclassification rate between classes.