## 5 Conclusions  
This study developed a multi-class classification model for predicting speech audiometry (SA) using pure-tone audiometry (PTA) data and compared the performance of machine learning and deep learning models. To achieve this, deep learning models such as MLP and RNN, as well as machine learning models like gradient boosting and XGBoost, were implemented. The models were evaluated based on accuracy, F1 score, and log loss, while a confusion matrix was analyzed to compare detailed classification performance and identify the most suitable model for hearing data prediction. Experimental results showed that the gradient boosting model achieved the highest accuracy of 86.22%, demonstrating superior classification performance. The XGBoost model recorded an accuracy of 86.04%, but achieved a higher F1 score, indicating more balanced classification performance across classes. In contrast, deep learning-based models, MLP and RNN, showed relatively lower performance, with accuracies of 85.77% and 85.41%, respectively. The RNN model in particular was limited in effectiveness due to the irregular nature of PTA data, preventing it from fully utilizing its recurrent structure. Additionally, all models exhibited lower prediction performance for class 2 (borderline hearing levels), likely due to overlapping data distributions causing some misclassifications. Nevertheless, both machine learning and deep learning models demonstrated a reasonable level of predictive performance, with gradient boosting and XGBoost in particular achieving high accuracy and balanced classification, thereby validating the feasibility of using PTA data for speech audiometry prediction. These findings suggest that deep learning models are not always the optimal choice for predicting speech audiometry using PTA data, and tree-based machine learning models may serve as a practical alternative. Specifically, because PTA data do not exhibit strong sequential patterns, RNN did not perform as expected, whereas gradient boosting and XGBoost, which effectively capture relationships between variables, proved to be more suitable. This underscores the importance of selecting the appropriate model for hearing data analysis, emphasizing that different types of data require tailored algorithmic approaches. Clinically, the results of this study indicate the potential for machine learning models to be utilized as reliable assistive tools in hearing assessment and hearing aid fitting. Traditional audiological evaluations often rely on expert assessments, but the proposed models can serve as an automated diagnostic aid, supporting hearing loss evaluation and treatment planning. Future research should focus on improving data quality through feature engineering and hyperparameter optimization to further enhance model performance. Additionally, incorporating hybrid models that combine machine learning and deep learning or leveraging ensemble techniques to maximize predictive capabilities could be considered. By doing so, this study's findings may contribute to the practical clinical application of PTA-based speech audiometry prediction models, ultimately providing a more precise foundation for hearing assessment. **Author Contributions:** Conceptualization, M.H. and S.J.C. ; methodology, J.s.S. and M.H. ; software, J.s.S. ; validation, J.s.S., J.M., M.M., S.y.K., N.-J.S. and S.J.C. ; formal analysis, J.s.S., J.M., M.M. and S.y.K. ; investigation, J.s.S. ; resources, J.s.S. and S.J.C. ; data curation, J.s.S., J.M., M.M., S.y.K., N.-J.S. and S.J.C. ; writing--original draft preparation, J.s.S. ; writing--review and editing, M.H., J.M., M.M., S.y.K., N.-J.S. and S.J.C. ; visualization, J.s.S. ; supervision, M.H. ; project administration, S.J.C. ; funding acquisition, M.H. and S.J.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by BK21 FOUR (Fostering Outstanding Universities for Research) Grant, No. 5199990914048 and supported by the Soonchunhyang University Research Fund.