## 3 Results
### Multivariate Analysis
#### 3.2.2 Multivariate Classification Based on Different Grades of the Disease  
Table 5 presents the results, represented in a confusion matrix, for each interviewer and classifier, according to the following different grades defined for the disease: no disease (0), mild (1), and moderate (2). This table provides information on the number of classified samples and their percentage of the total.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Classifier** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specific [\%]** \\ \hline \multirow{4}{*}{Automatic interviewer} & LDA & 55.82\% & 57.66\% & 54.35\% \\  & Logistic & 59.44\% & 60.36\% & 58.70\% \\  & KNN (n = 1) & 52.21\% & 46.85\% & 56.52\% \\  & KNN (n = 3) & 48.19\% & 39.64\% & 55.07\% \\  & KNN (n = 5) & 54.22\% & 44.14\% & 62.32\% \\ \hline \multirow{4}{*}{Human interviewer} & LDA & 47.89\% & 48.00\% & 47.83\% \\  & Logistic & 57.75\% & 60.00\% & 56.52\% \\ \cline{1-1}  & KNN (n = 1) & 47.89\% & 28.00\% & 58.70\% \\ \cline{1-1}  & KNN (n = 3) & 53.52\% & 24.00\% & 69.57\% \\ \cline{1-1}  & KNN (n = 5) & 43.66\% & 60.00\% & 34.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy, sensitivity, and specificity values for the human and automatic interviewers based on the multivariate classification using LDA, logistic classifier, and kNN according to the absence or presence of disease.  
\begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline  & \multicolumn{6}{c}{**Automatic Interviewer**} & \multicolumn{3}{c}{**Human Interviewer**} \\ \hline \multirow{2}{*}{**Classifier**} & **True** & \multirow{2}{*}{**0**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**0**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**Total**} \\  & **Disease** & & & & & & \\ \hline \multirow{4}{*}{LDA} & 0 & 75 (54.35\%) & 63 (45.65\%) & 138 (100\%) & 22 (47.83\%) & 24 (52.17\%) & 46 (100\%) \\  & 1 & 47 (42.34\%) & 64 (57.66\%) & 111 (100\%) & 13 (52.00\%) & 12 (48.00\%) & 25 (100\%) \\  & Total & 122 (49.00\%) & 127 (51\%) & 249 (100\%) & 35 (49.30\%) & 36 (50.70\%) & 71 (100\%) \\ \hline \multirow{4}{*}{Logistic} & 0 & 81 (58.70\%) & 57 (41.30\%) & 138 (100\%) & 26 (56.52\%) & 20 (43.48\%) & 46 (100\%) \\  & 1 & 44 (39.64\%) & 67 (60.36\%) & 111 (100\%) & 10 (40.00\%) & 15 (60.00\%) & 25 (100\%) \\  & Total & 125 (50.20\%) & 124 (49.80\%) & 249 (100\%) & 36 (50.70\%) & 35 (49.30\%) & 71 (100\%) \\ \hline \multirow{4}{*}{KNN (n = 1)} & 0 & 78 (56.52\%) & 60 (43.48\%) & 138 (100\%) & 27 (58.70\%) & 19 (41.30\%) & 46 (100\%) \\  & 1 & 59 (53.15\%) & 52 (46.85\%) & 111 (100\%) & 18 (72.00\%) & 7 (28.00\%) & 25 (100\%) \\  & Total & 137 (55.02\%) & 112 (44.98\%) & 249 (100\%) & 45 (63.38\%) & 26 (36.62\%) & 71 (100\%) \\ \hline \multirow{4}{*}{KNN (n = 3)} & 0 & 76 (55.07\%) & 62 (44.93\%) & 138 (100\%) & 32 (69.57\%) & 14 (30.43\%) & 46 (100\%) \\  & 1 & 67 (60.36\%) & 44 (39.64\%) & 111 (100\%) & 19 (76.00\%) & 6 (24.00\%) & 25 (100\%) \\ \cline{1-1}  & Total & 143 (57.43\%) & 106 (42.57\%) & 249 (100\%) & 51 (71.83\%) & 20 (28.17\%) & 71 (100\%) \\ \hline \multirow{4}{*}{KNN (n = 5)} & 0 & 86 (62.32\%) & 52 (37.68\%) & 138 (100\%) & 16 (34.78\%) & 30 (65.22\%) & 46 (100\%) \\ \cline{1-1}  & 1 & 62 (55.86\%) & 49 (44.14\%) & 111 (100\%) & 10 (40.00\%) & 15 (60.00\%) & 25 (100\%) \\ \cline{1-1}  & Total & 148 (59.44\%) & 101 (40.56\%) & 249 (100\%) & 26 (86.62\%) & 45 (63.38) & 71 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of the multivariate classification using LDA, logistic classifier, and KNN based on the presence or absence of disease.  
Table 6 translates the values from the confusion matrix into values of accuracy, sensitivity, and specificity obtained for each of the classifiers and interviewers. From them, it can be extracted that, once again, the best results were achieved with the logistic classifiers independently of the interviewer used.