# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 4 Discussion
### Analysis of Results
Dataset scaling presented an added problem, as in most of the cases the classification methodology, would require model re-training from the scratch, as the solutions provided were strongly dependent on the specific classification models and databases used, raising reasonable doubts about their potential performance when tested on more general scenarios, this being the case of some EE approaches. This concern calls upon the growing need for benchmarking databases, providing larger and wider datasets covering multiple voice pathology cases to approach the problem of detection and classification meeting reliable clinical standards. A common problem affecting MD and DD approaches studied is that of data preprocessing, comprising initial data cleaning (removal of unreadable data structures), elimination of gaps (imputation), categorical feature encoding, data normalization, and outlier removal, among other issues. Although this problem affects mainly big databases, it is seldom seen in laryngeal pathology assessment, and besides the automatic solutions that have been proposed in this sense, the issue remains one to be concerned about [83, 84]. In any case, these considerations might put under question the claim that EE approaches are of immediate application and free from tweaking or handcrafting. As a consequence of the review results and observations, the following methodological recommendations availing good practice procedures are to be taken into account helping to ensure that robust, reliable, and efficient experimental frameworks are granted for the research in the field of laryngeal pathology assessment:  
* **Size and balance of the databases**. Sample size should support reasonable statistical relevance to results. Demographic information should be provided (age, gender, co-morbidities). Inclusion and exclusion criteria are to be explicitly declared. Age matching between pathological and control participants must be ensured. Equal or balanced representation of pathology sample sizes should be sought to prevent majority/minority skew and other undesired effects. Special care has to be taken if certain data-enhancing techniques, such as data augmentation, are implemented. In this sense, generative adversarial networks (GANs) might provide a way to learn deep representations without extensively annotated training data, as an alternative to other data augmentation methods [85, 86]. * **Gender separation**. Special care needs to be taken in this regard, considering males and females separately as far as feature extraction and pattern matching are concerned, or using instead gender-sensitive feature normalization methods. This requirement is based on the sexual dimorphism conditioning the physiology of respiratory, phonation, and resonance systems strongly [45, 46], and consequently introducing important skewness in features, which may lead to erroneous or inaccurate results. For the same reasons, when a high-performance demanding analysis is sought, as in singing voice, data have to be separated according to phonation mechanisms (as M1/M2). * **Database recording methods**. Instrumentation, especially in microphone characteristics, room acoustics, recording levels, background noise, digitalization, downsampling, power-line filtering, and other disturbing factors, are to be carefully documented [87]. Procedures should have to be certified according to agreed criteria, e.g., Level 0 = unspecified, Level 1 = acceptable for perceptual assessment only, Level 2 = acceptable for fundamental frequency and level analysis with low noise, Level 3 = highest quality, anechoic, amenable for sensitivity analyses. * **Methodological issues regarding feature extraction**. Feature semantic load is to be sought and preserved, and feature selection must contribute originality to the research, provided that significant improvements are shown not only from the ML perspective but also from the clinical explainability point of view. * **Problem orientation**.