# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 1 Introduction  
Early detection of diseases is crucial for medical and healthcare professionals to provide timely interventions with proactive preventive measures. Voice disorders, which affect millions of people around the world, are characterized by abnormal deviations in the voice, observable through changes in pitch, volume, loudness, voice quality, fatigue, and vocal flexibility. These disorders typically manifest through rough vocal quality, breathiness, terminolussness, and pulsed phonation [1]. Artificial intelligence (AI)-driven solutions, therefore, assist voice pathologists and healthcare providers in automatically detecting and diagnosing pathological voices more efficiently and at lower costs [2]. Feature selection is a crucial preprocessing step before developing any machine learning or deep learning model [3]. The presence of irrelevant, redundant, or high-dimensional features can degrade the performance of algorithms. The objective of feature selection is to identify the optimal set of features that enhances algorithm performance. Feature selectiontechniques can be classified into filter-based, wrapper-based, and embedded-based approaches. Filter-based methods are based on ranking and statistical techniques for feature scoring and selection. On the other hand, wrapper methods utilize a learning algorithm during training to evaluate feature subsets. Lastly, embedded methods, such as genetic programming, learn to predict while training [3]. The challenge with feature selection arises when the number of features is large (i.e., \(N\)), thus, the search space becomes exponentially complex, with a size of (\(2^{N}\)). Searching for the optimal feature set in such a vast space becomes computationally expensive, time-consuming, and requires more energy. One of the earlier approaches to address feature selection is the metaheuristic search algorithms. Metaheuristics are stochastic search algorithms incorporating randomization into the search process [4]. Bio-inspired algorithms are metaheuristics and are widely used to solve the feature selection problem [5]. Bioinspired algorithms dynamically search for the best set of features, which enables them to explore various regions in the search space and have more diverse solutions. Bio-inspired algorithms use randomization during the search for the best feature set, allowing exploration of different areas of the search space and leading to more diverse solutions. These algorithms balance exploration and exploitation. Exploration ensures that the algorithm searches globally and avoids getting trapped in local optima, while exploitation focuses on local search to refine solutions. This paper aims to compare and find the best-performing combination of evolutionary optimizers and machine-learning classifiers for the optimization of feature selection and detection of voice disorders. Thus, the problem of feature selection to improve voice pathology detection is formulated as a multi-objective minimization problem, with two objectives: minimizing the number of features and the error rate. To this end, three well-regarded multi-objective evolutionary algorithms were utilized: NSGA-II [6], SPEA-II [7], and MOEA/D [8]. In addition, four machine learning algorithms were experimented with and served as base learners or internal assessors. The algorithms include the KNN, SVM, MLP, and RF. The experiments were conducted using three datasets, which are the Speech Language Impairment (SLI) [9], Saarbruecken Voice Database (SVD) [10], and the VOice Icar fEDerico II database (VOICED) [11]. Various features were extracted from the voice utterances, including the Mel Frequency Cepstral Coefficients (MFCCs), spectrograms, Linear Predictive Coefficients (LPC), and others [12]. Nonetheless, measuring how ascertain predictive models are in their predictions is important to disclose their reliability in different tasks, such as diagnosing diseases, detecting fraudulent transactions, and others [13].