## 4 Results
### Discussion  
In this study, various machine learning and deep learning models were developed and compared to solve the multi-class classification problem of predicting speech audiometry using PTA data. The models used included MLP, RNN, gradient boosting, and XGBoost, and their performance was evaluated using accuracy, F1 score, and log loss, as shown in Table 5. A confusion matrix was also used to analyze each model's predictive performance, highlighting their strengths and limitations. On the test dataset, the MLP model achieved an accuracy of 85.77%, demonstrating relatively strong classification performance. It performed well in class 0 and class 1, but its classification accuracy for class 2 was lower, leading to confusion between classes. The RNN model recorded an accuracy of 85.41%, and despite its structural advantage in learning sequential patterns, exhibited higher misclassification between class 1 and class 2. This suggests that RNN may have limitations in fully capturing the characteristics of PTA data.  
When comparing gradient boosting and XGBoost, the gradient boosting model achieved the highest accuracy of 86.22%. Still, it exhibited a high false-negative rate for class 2, indicating that the model tended to misclassify some data points. On the other hand, the XGBoost model recorded an accuracy of 86.04%, slightly lower than gradient boosting, but achieved a higher F1 score, indicating more balanced classification performance across classes. Notably, XGBoost was more effective in reducing false negatives for class 1, demonstrating its ability to mitigate overfitting to specific classes better than gradient boosting. The comparison of the models' performance suggests that machine learning-based models (gradient boosting and XGBoost) outperformed deep learning-based models (MLP and RNN) in accuracy. This implies that PTA data may lack the complexity required for deep learning models to learn effectively and tree-based boosting methods  
Figure 10: Gradient boosting and XGBoost confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Loss** & **F1 Score** & **Time** \\ \hline MLP & 85.77\% & 0.3181 & 0.8596 & 91 s \\ RNN & 85.41\% & 0.3796 & 0.8548 & 102 s \\ Gradient & 86.22\% & 0.3083 & 0.8635 & 297 s \\ Boosting & 86.04\% & 0.3056 & 0.8619 & 34 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: DL and ML performance evaluation.  
may be more suitable for capturing the relationships between variables. Additionally, since PTA data exhibit relatively low temporal dependency, RNN did not provide the expected performance improvement. The findings of this study suggest that machine learning models can serve as practical alternatives for predicting speech audiometry using PTA data. However, additional data preprocessing and feature engineering may be necessary to address overlapping class distributions. Future research could focus on extracting more effective features, optimizing model hyperparameters, and improving performance. Additionally, ensemble techniques that combine the strengths of multiple models could be explored, along with incorporating a more diverse patient dataset to assess the real-world applicability of these models in clinical environments.