# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 5 Discussion  
The main goal of this study was to equip otolaryngologists with a transparent technique for analysis of voice and query data capable of providing support in screening for laryngeal disorders. In this work, screening concerns pathology detection through classification of subject's data into _'healthy'_ and _'pathological'_ classes as well as exploration of data and automatic decisions. To the best of our knowledge, none of the existing techniques exhibit such properties.  
To achieve transparency, automatic decisions are obtained from a set of association rules extracted from query data and a decision tree designed using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice. Accurate pathology detection was observed on unseen subjects: EER of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The obtained EER rates are lower compared to those achieved using sophisticated 'black box' techniques based on voice data only [22].  
Besides a class label, the classification techniques also provide the level of confidence in the decision they suggest. The low EER achieved by the association rules-based classifier indicates high preventive health-care potential available in questionnaire data. These findings indicate that relevantly phrased queries can serve as very useful sensors in characterizing subject's health and are of great value in the education process of otolaryngology students.  
Figure 6: Probability (\(y\)-axis) density functions of query parameters for _healthy_ (blue) and _pathological_ cases (red). A dashed line and numbers in the parentheses correspond to a parameter value of a subject randomly selected from the database used to train the developed tools.  
The suggested novel way to assess pairwise similarity of observations allows mitigating the influence of irrelevant variables, since only variables contributing to a decision are used. Therefore, visualizations of multidimensional data represented by such pairwise similarities are more robust to noise compared to visualizations based on ordinary distance measures e.g., Euclidian distance. The _t_-SNE algorithm proved to be a useful tool for obtaining 2D data maps from data similarities. Such 2D maps are very helpful in comparative studies and providing deeper insights into data representing various groups of subjects. Ability to relate basic voice and query parameters to a specific diagnosis as well as to parameters of similar (in terms of position on a 2D map) subjects helps laryngologists to make associations and generalization over different cases. A 2D map is also of great help in identifying erroneously labelled data and other unexpected deviations in both voice and query data.  
Probability density functions, created using the Epanechnikov kernel smoothing method, provide additional information, when evaluating patients with respect to audio or query data parameters. Visualization of probability density functions of the audio parameters, such as Jitter, Shimmer, HNR and SNR, was appreciated by otolaryngology specialists, since they use these parameters in their daily work. Probability density functions of query data provide additional information about a patient (in a statistical sense) and serve as valuable learning material for otolaryngology students. Decision trees provide additional insights concerning main voice parameters and are very useful for teaching/learning purposes.