# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 5 Conclusions  
This comparative research study presents a new multi-objective and bio-inspired machine learning optimization approach for improving the accuracy of voice disorder detection. Three datasets were utilized along with three multi-objective optimizers (NSGA-II, SPEA-II, and MOEA/D) and four base classifiers (RF, KNN, MLP, and SVM). In this study, the problem is formulated as a multi-objective feature selection, which aims to minimize the number of features and the error rate. Several spectral and time-domain features were extracted from SLI, SVD, and VOICED datasets and labelled as normal and abnormal. Comparing algorithms based on the IGD, HV, Spread, and Spacing metrics showed that the three optimizers exhibited competitive performance, while the NSGA-II algorithm disclosed a powerful capability for identifying pathological voices. Also, the best classifiers per dataset are KNN for SLI (best IGD & HV), MLP for SVD (best IGD), and SVM for VOICED (best IGD), with MLP also excelling in HV. Although NSGA-II \begin{table}
\begin{tabular}{l l l l} \hline
**Dataset** & **Accuracy** & **Nonconformity (Avg.)** & **Nonconformity (Std.)** \\ \hline SLI & 0.987 & 0.015 & 0.079 \\ SVD & 0.743 & 0.253 & 0.364 \\ VOICED & 0.706 & 0.318 & 0.350 \\ \hline \end{tabular}
\end{table} 
Table 10: Conformal predictions of NSGA-II and MLP. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Study** & **Method** & **Dataset** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline [PERSON] et al. [15] & CNN + SMOTE & SVD & \textgreater{}90\% & 94.3\% & - & - \\ [PERSON] et al. [16] & SVM + Feature Sel. & SVD, MEEI & 99.97\% & 99\% & - & - \\ [PERSON] et al. [18] & SL-RANN & SVD & 94.35\% & - & - & - \\ [PERSON] et al. [19] & FLN + MFCC & SVD & 84.64\% & 86.80\% & 97.39\% & 86.05\% \\ [PERSON] et al. [21] & AOA + CNN + SVM & PD & 96.62\% & - & - & - \\ [PERSON] et al. [23] & MASS-NN & PD (UCI) & 99.1\% & 99.5\% & - & - \\ Proposed Method & NSGA-II-based SVM & VOICED & 69.0\% & 81.2\% & 71.8\% & 93.3\% \\ Proposed Method & NSGA-II-based MLP & SVD & 63.9\% & 66.7\% & 53.8\% & 87.6\% \\ Proposed Method & NSGA-II-based KNN & SLI & 81.3\% & 86.1\% & 85.5\% & 86.7\% \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of Proposed Method with State-of-the-Art Approaches. with MLP is not always the best, it remains a strong choice. Furthermore, by integrating conformal prediction, this study ensures statistically feasible uncertainty quantification in feature-selected models, enhancing the reliability of voice disorder detection even in high-dimensional settings. Future research will investigate optimizing parameters and refining feature selection more closely, as these are important steps for promoting the performance of speech disorder detection. Incorporating advanced algorithms, such as deep neural networks, can further improve performance. Additionally, recent studies have shown the effectiveness of large language models (LLMs) in automatic speech recognition and speech analysis. Thus, this motivates the utilization of LLMs or speech large models for detecting and reconstructing corrupted speech. Nonetheless, a future work could refine conformal predictions by tackling calibration issues in lower-accuracy datasets (such as SVD and VOICED) or developing adaptive nonconformity scores to improve the precision of prediction sets. Conceptualization, P.G.-S., M.H. and V.V.-P.; Methodology, P.G.-S. and M.H. ; Validation, M.H., P.G.-S. and V.V.-P.; Data curation, M.H. ; Writing original draft preparation, M.H. ; Writing review and editing, P.G.-S. and V.V.-P.; Supervision P.G.-S. All authors have read and agreed to the published version of the manuscript. This research has been funded by the Spanish Ministry of Science, Innovation and Universities MICIU/AEI/10.13039/501100011033 under project number PID2023-147409 NB-C21. The three datasets are available online and they are open-source.