## 4 Discussion  
### Literature Review  
Voice features have been interesting subjects for researchers developing ML algorithms capable of classifying voice-related pathologies. Within this frame, different databases were specifically adopted. As an example, [PERSON] et al. [39] used The Massachusetts Eye and Ear Infirmary (MEEI) database that con \begin{table}
\begin{tabular}{c c c c c} \hline
\begin{tabular}{c} Correlation and IG are similar in performance \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: Results of all statistical inferences. Abbreviations: NB = Naïve Bayes, MLP = Multi-layer Perceptrion, RF = Random Forest. sists of 53 healthy and 657 pathological speakers with different pathologies (e.g., ASD, conversion dysphonia, erythema, and hyperfunction), with the speaker uttering the vowel/a/. A second database was used, termed Principe de Asturias (PdA), which consists of 239 healthy and 200 pathological speakers with different pathologies (e.g., nodules, polyps, oedemas, and carcinomas), every speaker uttering the sustained vowel/a/. A third database was the so-called Czech Parkinsonian Speech Database (PARCZ), which consists of 52 healthy speakers and 57 PD patients who suffer from hypokinetic dysarthria, all speakers uttering the vowel/a/. Feature extractions were done on assumed features for phonation, tongue movement, speech quality, segmental features, spectrum, wavelet decomposition, empirical mode decomposition, non-linear dynamics, and high level. A non-parametric Mann-Whitney U test was used for feature selection. Support Vector Machine (SVM) and RF classifiers were used for binary classification (pathological versus healthy). The accuracy of their results were 100% (MEEI), 82.1% \(\pm\) 3.3% PdA, and 67.9% \(\pm\) 6% (PARCZ), respectively. [PERSON] et al. [40] used the Saarbruuecken Voice Database with 2000+ voice recordings sampled at 50 kHz, out of which 687 are collected from HSs (428 females and 259 males) and 1356 are collected from subjects (629 males and 727 females) with voice disorders. This database contains 71 different voice disorders. Each recording session consists of a German sentence and vowels of/a/,/i/, and/u. Only vowel vocal tests were used by the researchers who chose only six diseases from the dataset (spasmodic dysphonia, recurrent laryngeal nerve palsy, functional dysphonia, and psychogenic dysphonia, laryngitis, and leukoplakia). For feature extraction, they used the Interspeech Computational Paralinguistics Challenge features set (ComParE 2013), Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), MFCCs, Perceptual Linear Prediction (PLP), Glottal, Intonation, MFCC-Residual, and MFCC-ZFF (zero frequency filtering). A SVM classifier is used with five-fold cross-validation and binary classification. The best reported accuracy was 85.2%. [PERSON] et al. [41] used the Saarbruuecken Voice Database. They chose all diseases and vowel/a/from the vocal tests. The features extracted were F0, jitter, shimmer, HNR, and MFCC. Feature selection was performed using correlation and IG. For classification they used SVM, Decision Tree (DT), Bayesian Classification (BC), Logistic Model Tree (LMT), and Instance-based Learning Algorithms (IBLA). The best reported result was an accuracy of 85.77% with the SVM binary (pathological versus healthy) classifier. [PERSON] et al. [42] used data from de novo patients recorded at the \"Hospital das Clinicas\" of the Medical School of Sao Paulo University. The database comprised 65 recordings for vowel/a/and have four groups: control (12 male and 8 female), [PERSON]'s edema (2 males and 14 females), vocal nodules (2 males and 13 females), and neurologic diseases (7 males and 7 females). They used MFCC for feature extraction. For feature selection, they relied on statistics of the time series (voice segment) to search for a subset with greater discriminating power. SVM and K-Nearest Neighbors (KNN) were used for classification and cross-validation was applied.