# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 4 Experiments and Results
### Exploring Data and Decisions  
Data similarity values, computed using Equations (8) and (9), were averaged and collected into a similarity/proximity matrix. The proximity matrix was then mapped onto the 2D space using the \(t\)-SNE algorithm. The perplexity parameter of the \(t\)-SNE algorithm was chosen empirically and set to 50. The algorithm converged after 320 iterations. The resulting \(t\)-SNE visualization is shown in Figure 5, where 'triangles' stand for healthy and'squares' for pathological cases. Filled circles denote two selected cases, assigned to the 'healthy' class by the classifier, used for tracking purposes. The light-filled circle stands for one subject and the dark-filled one for another subject.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question \#** & **Name \& Value** & **Healthy** & **Pathological** \\ \hline
22–25 & \(G_{0}=0\) & 11 & 0 \\
11 & H = 2 & 11 & 0 \\
6 & C = 0 & 6 & 1 \\
7 & Y = 0 & 6 & 0 \\
4 & U = 7 & 3 & 2 \\
8 & MPT = [2,12] & 0 & 4 \\
21 & L = 3 & 4 & 0 \\
9 & VAS = (63,100] & 0 & 2 \\
22–25 & \(G_{0}=0\) & 0 & 1 \\
20 & X = (73,100] & 0 & 1 \\
19 & S = (75.8,100] & 0 & 1 \\
18 & R = (60,100] & 0 & 1 \\
16 & W = (60,100] & 0 & 1 \\
15 & D = (65,100] & 0 & 1 \\
1 & Gender = F & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rule coverage for mined antecedent items. Absolute frequency (gravity), as the number of rules the specific response participates in, is in “Healthy” and “Pathological” columns.  
A 2D map created by the \(t\)-SNE algorithm facilitates selection of specific cases for deeper studies and comparison. The map allows spotting misclassified, incorrectly labelled observations or observations mapped very closely, but coming from different classes and requiring deeper analysis. The determined class label and classification certainty obtained from association rules and decision tree are linked to each observation. The observation labelled by the light-filled circle in Figure 5 illustrates one such case. The observation was assigned to the 'healthy' class by the classifier, but was mapped in the area mainly occupied by 'pathological' cases. Such observations are candidates for deeper exploration and the map lends itself for such analysis. By clicking on a selected observation on the 2D map, the user accesses information linked to the selected individual: the six voice parameters (F0, Jitter, Shimmer, NNE, HNR and SNR) and questionnaire data. The observation denoted by the dark-filled circle in Figure 5 represents a correctly classified 'healthy' subject.  
The class-conditional probability density functions of audio and query parameters are of great help in such kind of analysis. Figure 6 presents examples of class-conditional probability density functions, estimated using the Epanechnikov kernel smoothing, of query parameters. Density functions of the age parameter, see the 1 st row 2 nd column in Figure 6, illustrate deficiency in data collection pointing out the lack of healthy subjects in a certain age group. The class-conditional density functions also show how a big difference can be expected between values of a certain parameter computed using subjects from the _healthy_ and _pathological_ classes. More than one mode in the distribution hints at the existence of clusters in the data.  
Figure 5: Visualization of observations, represented by proximity values, by the \(t\)-SNE algorithm.