# 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  
We extend the evaluation of the approaches tested in Experiment 1 by applying them to a range of classification tasks: four-way classification (ND vs. MCI vs. FMD vs. HC) and several two-way classifications (ND vs. MCI, ND vs. FMD, MCI vs. FMD, ND vs. HC, MCI vs. HC, FMD vs. HC). These tasks are investigated using both the SD and IQR approaches, with their default factors as well as the factors that achieve the highest performance. The four-way classification results of using both the SD and IQR approaches with their default and optimal factors are shown in Table 4. From Table 4, it can be seen that the IQR achieves the highest performance and that changing the factor shows no difference in the obtained results. It is observed that the detection of participants with MP (e.g., ND vs. MCI, ND vs. FMD, and MCI vs. FMD) is better when the IQR is used. In contrast, the performance decreases when the SD is used, especially for FMD participants classified as HC and MCI. This observation supports the conclusion reached in previous work that classifying FMD participants is challenging even in the clinic [33]. On the other hand, using the SD improves the detection of the HC group from the ND, MCI, and FMD groups. It can be seen that the SD's UB makes it challenging to distinguish between ND and MCI. Regarding the two-way classification, the experiment is examined from two aspects: (1) differentiating people with MPs from each other (ND vs. MCI, ND vs. FMD, MCI vs. FMD) and (2) distinguishing each group with MPs from HCs (ND vs. HC, MCI vs. HC, FMD vs. HC). The results of aspect one, classifying individuals with memory-related problems, are presented in Table 5. As mentioned above, IQR gives better results in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Approach** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{2}{*}{a, b = 1.5} & SD (Min-UB) & 44\% & 35\% & 43\% & 38\% \\  & IQR & **49\%** & **53\%** & **49\%** & **49\%** \\ \hline \multirow{2}{*}{a = 3, b = 2} & SD (Min-UB) & 42\% & 32\% & 41\% & 35\% \\  & IQR & **49\%** & **54\%** & **49\%** & **49\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of four-way classification—neurodegenerative disorder (ND) vs. mild cognitive impairment (MCI) vs. functional memory disorder (FMD) vs. healthy controls (HCs)—using two factors for standard deviation (SD) a = 3 as the default value and a = 1.5 as the factor with the highest performance, and for interquartile range (IQR) b = 1.5 as the default value and b = 2 as the factor with the highest performance. These approaches are tested on 70 thresholds (features) using linear SVM. Figure 9: The confusion matrices for the two-way classification of healthy controls (HCs) vs. memory problems (MPs) using two approaches to detect the upper boundary (UB) with a range of factors applied to 70 features or thresholds. In the confusion matrix, darker colors indicate higher true predicted values, while lighter colors indicate lower predictions (MP: includes ND, MCI, and FMD; rows: true labels; columns: classified labels). classifying MP classes. The highest performance between the three classification tasks is achieved with 72% to classify ND and MCI from each other. Using the SD causes an incorrect classification for all of the FMD group regardless of the factor value, as presented in Figure 10.  
The second aspect of the two-way classification is testing the system performance in classifying every class with MPs from the HC class (ND vs. HC, MCI vs. HC, and FMD vs. HC). Table 6 shows the results of the system performance in each classification task for both approaches and different factor numbers. It can be seen that using the SD approach gives a much better performance than the IQR approach. In ND vs. HC, increasing the threshold range gives better classification performance from factor a = 1.5 with an accuracy of 69% to a = 3 with 78%.