## 3 Results
### Feature Selection  
As can be seen in Table 8, the results obtained during the feature selection process remained relatively constant, with the kurtosis and skewness features being irrelevant regardless of the interviewer used and the type of classification.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Classifier** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline \multirow{4}{*}{Automatic interviewer} & LDA & 38.55\% & 63.96\% & 46.38\% \\  & Logistic & 46.59\% & 69.37\% & 50.72\% \\  & KNN (n = 1) & 43.78\% & 46.85\% & 56.52\% \\  & KNN (n = 3) & 36.55\% & 54.95\% & 42.75\% \\  & KNN (n = 5) & 34.94\% & 73.87\% & 26.09\% \\ \hline \multirow{4}{*}{Human interviewer} & LDA & 40.85\% & 56.00\% & 39.13\% \\  & Logistic & 56.34\% & 72.00\% & 54.35\% \\ \cline{1-1}  & KNN (n = 1) & 48.48\% & 28.00\% & 65.85\% \\ \cline{1-1}  & KNN (n = 3) & 25.35\% & 72.00\% & 19.57\% \\ \cline{1-1}  & KNN (n = 5) & 30.99\% & 68.00\% & 30.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy, sensitivity, and specificity values for the automatic and human interviewers based on the multivariate classification using LDA, logistic classifier, and kNN for different disease grades: no disease (0), mild disease (1), and moderate disease (2).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**MANOVA**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Disease** (HC *-AD)} & \multicolumn{2}{c}{**Grade** (HC-AD1 *)**} & \multicolumn{2}{c}{**Grade (HC-AD2 *)**} & \multicolumn{2}{c}{**Grade (AD1-AD2)**} \\ \cline{2-7}  & \multicolumn{2}{c}{\(p\)**-Value**} & \multicolumn{2}{c}{\(p\)**-Value**} & \multicolumn{2}{c}{\(p\)**-Value**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \hline \multicolumn{7}{l}{**Statistic/ Interviewer**} & **H** & **A** & **H** & **A** & **H** & **A** & **H** & **A** \\ \hline W * & 0.14 & 0.01 & 0.12 & 0.05 & 0.40 & 0.06 & 0.35 & 0.74 \\ P * & 0.14 & 0.01 & 0.12 & 0.05 & 0.40 & 0.06 & 0.35 & 0.74 \\ L * & 0.14 & 0.01 & 0.12 & 0.05 & 0.40 & 0.06 & 0.35 & 0.74 \\ R * & 0.14 & 0.01 & 0.12 & 0.05 & 0.40 & 0.06 & 0.35 & 0.74 \\ \hline \hline \end{tabular}  
*HC: healthy control; AD1: mild-grade Alzheimer’s; AD2: moderate-grade Alzheimer’s; W: [PERSON] lambda; P: [PERSON]'s trace; L: [PERSON] trace; R: [PERSON]’s largest root; H: human interviewer; A: automatic interviewer. The statistics indicating that the set of variables does not differentiate among the different AD groups are highlighted in grey.  
\end{table}
Table 7: Multivariate MANOVA analysis: comparison between automatic and human interviewers and the different AD populations. [PERSON] lambda, Lawley–Hotelling trace, [PERSON]’s trace, and [PERSON]’s largest root statistics applied to the set of emotional temperature measures.  
In this way, we decided to use the set of samples from the automatic interviewer and the binary classification as a reference for our analysis. Table 9 shows the results of the emotional temperature feature selection process under these conditions.  
Based on the values presented in Table 9, the results of the feature selection process applied to emotional temperature measures are displayed in Figure 1, visually representing the weight of each feature in the set.  
The results indicate that the best measures, by a wide margin over the other features, were TEd (\(te_{d}\)) and MediaTEc (\(\overline{te_{c}}\)). Although, to a lesser extent, VarTEc (\(\sigma_{te_{c}}^{2}\)) also proved to be relevant to the set. In any case, the most relevant feature was MediaTEc (\(\overline{te_{c}}\)).