# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 2 Related Works  
The detection of voice disorders through machine learning and deep learning techniques has been explored extensively in recent years, as demonstrated in multiple studies. In a notable work by [PERSON] et al. [15], the authors focused on the extraction of hand-crafted features, such as energy, entropy, and zero-crossing rate, from the SVD dataset for multi-label detection of voice disorders. They trained several machine and deep learning models, using the Synthetic Minority Oversampling Technique (SMOTE) for oversampling, and found that the convolutional model achieved the highest accuracy of 94.3%. This success highlights the efficacy of machine learning techniques in the automatic detection of voice disorders. Similarly, [PERSON] et al. [16] utilized several machine learning algorithms, such as decision trees, random forests, and support vector machines (SVM), specifically to detect dysphonia. They worked with both the SVD and Massachusetts Eye and Ear Infirmary (MEEI) datasets, applying filter-based feature selection, and achieved an impressive accuracy of 99.97% with the SVM classifier. These studies underscore the effectiveness of machine learning methods for voice disorder detection. However, the authors in [17] applied Inductive CP with RF to improve the confidence and reliability of silent speech recognition using surface electromyography by providing valid uncertainty estimates with guaranteed error rates. Also, it introduced test-time data augmentation, utilizing unlabeled data to improve prediction performance. This emphasizes the potential of ICP for data augmentation and real-time applications in silent speech recognition. Further contributions to the field include [PERSON] et al. 's work [18], where they trained a Sequential Learning Resource Allocation Neural Network (SL-RANN) to detect vocal cord disorders, using discrete Fourier transforms and Mel Frequency Cepstral Coefficients (MFCCs) for preprocessing. The approach achieved a commendable accuracy of 94.35%. Similarly, [PERSON] et al. [19] used Fast Learning Networks (FLN) in conjunction with MFCC features extracted from the SVD database, achieving a g-mean of 86.81%. Additionally, [PERSON] et al. [20] conducted a comprehensive review of deep learning approaches for voice disorder detection, identifying the prevalent use of deep learning for clinical diagnosis and assistive technologies. They also highlighted key gaps in current research, such as the lack of multi-modal approaches, the need to account for variabilities in speech signals (e.g., age, gender, and accent), and the absence of generalizable models that can detect multiple voice disorders simultaneously. In the realm of optimization for voice disorder detection, several studies have employed evolutionary algorithms. For example, [PERSON] et al. [21] proposed an arithmetic optimization algorithm (AOA) for detecting [PERSON]'s disease (PD), using feature maps extracted by eight convolutional neural network (CNN) architectures from spectrogram images. This approach, combined with machine learning algorithms like KNN and SVM, achieved an average accuracy of 96.62%. Similarly, [PERSON] et al. [22] introduced a whale-ant (WO-ANT) optimization algorithm for PD detection using speech samples, which achieved 90.29% accuracy. [PERSON] et al. [23] further explored PD detection using a neural network optimized with a multi-agent salp swarm (MASS) algorithm, obtaining an F1-score of 0.995. However, it is important to note that these studies were focused solely on [PERSON]'s disease. Also, authors in [24] enhanced the detection of [PERSON]'s disease by using the Particle Swarm Optimization to assign weights for the acoustic features based on their relevance to the classifiers. Using the Oxford PD dataset and 5-fold cross-validation, the method improved the classification performance, where KNN achieved an accuracy of 97.44% and sensitivity of 98.02%.