# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 2 ML Methods in Laryngeal Pathology Assessment
### Model-Driven vs. Data-Driven Assessment Systems  
The MD approach is the classical and oldest approach, maintaining that an artificial system to take decisions on recently acquired known data must be based on the knowledge extracted from a priori collected data (past historical observations, metadata, anamnesis, etc.) after a profound examination of apparent links between data classes and desired decision results deduced by logical methods such as induction, comparison, or reduction. The knowledge extracted used to be formalized in rules of the kind _if <condition!_ then <decision!_ else <decision!_. This was in keeping with the basis of medical practice, and as such, it was massively applied in the design of medical expert systems [32]. Implementations of this paradigm using Bayesian Inference, and Fuzzy Logic seemed to work quite well, but have a complex and costly design and testing practice, consuming lots of tweaking effort, and are not free from problems related to the gap between clinician semantics and engineering practice. The term \"knowledge engineering\" was created to define how to bridge that crucial semantic gap [33], and gave way to the first rule-based medical expert systems, MYCIN. The DD approach, on the other hand, is based on statistical methods to establish links between observable data and decision categorical output data, and it was very much fueled by the advent of digital computers, as it allowed the easy computation of correlation maps, conditional probabilities, likelihood associations, and complex numerical algorithms, establishing clustering relationships between observed data and desired categories or outcomes independently of rules. This approach capitalized on the seminal work of [PERSON] [34] and gave rise to the term Artificial Neural Networks (ANNs), a sort of DD system, as there was no need to know the rules governing the associations, but rather only the topological links and numerical structures populating them. These systems brought a lot of new studies, reporting spectacular results in classification and decision systems, eventually leading to an asymptotic concept known as EE systems, claiming the banishing of MD systems as old-fashioned and cumbersome. This controversial debate is not new, as it can be traced to three decades ago at least [35]. Essentially, MD stand on prior formal knowledge about the problems involved in pathological phonation (physiological, biomechanical, neuromotor, and acoustical) as far as airway passages, muscles, cartilages, bones, and other related tissues are involved in voice production [36]. The Handcrafter Feature (HF) process refers mainly to the extraction of features to be used in MD systems, accordingly to their semantic load (their a priori relationship to voice disorders). In turn, some DD systems may be driven by distributed modeling as well, and therefore they could be considered hybrid approaches. DD systems may also stand on a previous HF stage, giving rise to hybrid approaches, for instance, when using glottal features (open and close quotients) to train ANNs, SVMs, or other DD systems. In their turn, it is assumed that EE systems would not require a previous HF stage. From this point of view, EE would be the ideal DD system. The relationship among these concepts is better clarified in Figure 1.  
**Handcrafted Feature Approach**: The first stage of any AI-based detection or classification system is devoted to extracting the desired features from raw input data and well-known feature models, which will be the input to DD or MD approaches to generate the system results such as classification and performance scores. A typical example is that of mel-frequency cepstral coefficients (MFCCs) [37]. Their feature estimation model is based on windowing the acoustic signal, Fourier transform, logarithmic power spectrum, band-filter bank vectorization, and cosine transform.