# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 3 Methodology  
In this work, the pathology detection task is solved through classification of voice and query data into two classes, _healthy_ and _pathological_. Since the tools are to be used by laryngologists in clinical practice, data classification and exploration algorithms applied should lend themselves to provide insights into automatic decisions and numerical data (be transparent). The same applies to parameters characterizing subject's voice, sustained phonation of vowel _/a/_ in this case. Hundreds and thousands of parameters can be extracted for characterization of a subject's voice [22]. While the use of huge sets of parameters and advanced algorithms indicates how good detection performance can be achieved. Such tools are usually attributed to the \"black box\" category, since they provide very little insights into the data they analyse and the decisions they take. Therefore, to characterize a subject's voice, we used just six audio parameters widely adopted by laryngologists all over the world: fundamental frequency (F0), jitter, shimmer, normalized noise energy (NNE), harmonics-to-noise ratio (HNR), and signal-to-noise ratio (SNR). F0 measures how low or high the frequency of subject's voice is; jitter assesses the periodicity of vocal fold vibrations; shimmer reflects the cycle-to-cycle variability in amplitude of the voice; NNE quantifies the relative level of vocal noise (compared to the harmonics); HNR quantifies the amount of noise in the voice signal; and SNR compares the voice signal total energy to the energy of the aperiodic component of the signal. There are several variants of the parameters based on signal-analysis techniques applied to compute them. In this work, the utterance-based approach was used to compute the audio parameters. Information on how these parameters can be computed can be found in [12; 25]. A set (of a similar size) of other voice parameters could probably provide higher classification accuracy. However, the aforementioned six parameters are already widely used to quantify dysphonia changes and characterise outcomes of therapeutic and surgical treatment of laryngeal diseases. Two transparent techniques were developed for pathology detection: _association rules_ for analysis of query data and a decision tree for exploring voice parameters. Decision trees are popular classification tools in various medical applications. Decision trees have also been used for voice pathology detection too [26; 27]. The main novelty of this work is the suggested way of combined use of association rules and decision trees for voice pathology detection based voice and query data. A novel approach to data exploration is also suggested. The C4.5 decision tree [28], applied in this work, was created using the six audio parameters augmented with the glottal function index (GFI) parameter from the query data. The GFI parameter was added due to its high frequency of participation in various association rules [13]. According to [PERSON] et al. [24], the GFI is a reliable and reproducible symptom index, which is easy to administrate and exhibits good validity. The index is useful in the assessment of patients with glottal dysfunction. The Matlab (version R2012b, MathWorks, Natick, Massachusetts, USA) environment was used to create the decision tree. One can expect obtaining similar results using the Weka tool [29], trees.J48, which is a clone of the C4.5 decision tree learner. Association rules, identifying the strongest co-occurrences of answers, were mined using an unsupervised Apriori algorithm [30]. A simple confidence-based classifier was derived from association rules for detection of laryngeal disorders. Both association rules and a decision tree are transparent algorithms, based on parameters routinely used by otolaryngologists, and can greatly contribute to better understanding of various conditions and treatment planning. Ability to explore data and automatic decisions is of great value for an end user.