# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Performance Using Eye Blink Features
#### 4.1.2 Experiment 2: Extending Classification Evaluation Using Optimal SD and IQR Parameters
In contrast, reducing the threshold range from a = 3 to a = 1.5 enhances the performance from 78% to 89%, respectively. In FMD vs. HC, the SD approach has the same performance when the factor changes. It can be seen in Figure 11 that the  
Figure 10: The confusion matrices for the two-way classifications—neurodegenerative disorder (ND) vs. mild cognitive impairment (MCI), neurodegenerative disorder (ND) vs. functional memory disorder (FMD), and mild cognitive impairment (MCI) vs. functional memory disorder (FMD)—for both the standard deviation (SD) and the interquartile range (IQR), using 70 features or thresholds. In the confusion matrix, darker colors indicate higher true predicted values, while lighter colors indicate lower predictions. (rows: true labels; columns: classified labels). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classes** & **Approach** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline ND vs. MCI & SD (Min-UB) & 67\% & 68\% & 67\% & 66\% \\  & IQR & **72\%** & **72\%** & **72\%** & **71\%** \\ \hline ND vs. FMD & SD (Min-UB) & 56\% & 29\% & 50\% & 37\% \\  & IQR & **69\%** & **68\%** & **66\%** & **66\%** \\ \hline MCI vs. FMD & SD (Min-UB) & 54\% & 28\% & 50\% & 36\% \\  & IQR & **67\%** & **70\%** & **64\%** & **63\%** \\ \hline \hline Approach Classes & ND vs. MCI & ND vs. FMD & MCI vs. FMD \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of the two-way classifications—neurodegenerative disorder (ND) vs. mild cognitive impairment (MCI), neurodegenerative disorder (ND) vs. functional memory disorder (FMD), and mild cognitive impairment (MCI) vs. functional memory disorder (FMD)—using two factors for standard deviation (SD), a = 3 as the default value, and a = 1.5 as the factor with highest performance, and for interquartile range (IQR), b = 1.5 as the default value, and b = 2 as the factor with highest performance. These approaches are tested on 70 thresholds or features using linear SVM. predictions of these classes for both approaches explain which class is classified much better. It is observed that using the SD with a high factor (a = 3) increases the detection of ND participants from the HC group. In contrast, the HC participants could be detected better using IQR regardless of the factor value. Although MCI and HC are detected significantly using the SD with a low factor value (a = 1.5), only two participants are classified incorrectly, as seen in the confusion matrix. Interestingly, it can be seen that none of the MCI participants are ever confused with HC participants when the IQR approach is used. Regarding the FMD vs. HC classification, the HC participants are much better detected from the FMD group when the SD factor a = 1.5 or 3, and the IQR factor b = 2. The best factor for detecting FMD from HCs is achieved using a high value for the SD factor (a = 3), with only two FMD participants confused with the HC group. Taking all these findings into account, SD with a high factor value could help the model in the training phase to learn by capturing a pattern that could distinguish between these classes with MPs from the HC group. It can be seen that using a low value for the factor results in a model that confuses the two classes and cannot classify them from each other. In contrast, the detection of HC from other classes such as ND and FMD is much better when the IQR is used. It captures a smaller range than the SD, which leads to finding features from the model that identifies the HC from other classes. When the third quartile of the IQR approach is used to determine the UB, several classification tasks show better performance than when the 3 rd SD is used. However, there are also several classification tasks that show better performance when the 3 rd SD is used compared to using the IQR approach. Table 7 summarizes the different classification tasks and which UB-determining approach achieves the best performance with the significance test \(p\)-value.