# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 3 Results
### Ablation Study
This emerged not only by considering the accuracy metric but also by the sensitivity and precision rates which indicated a high percentage of correct classifications in both classes. For completeness, the confusion matrices of two classifiers (SVM-Q and NN-N) are shown in Figure 8 to better analyze these results. SVM-Q provided better results among all. It is also helpful to discuss the results of the KNN, SVM-L and SVM-C, which behaved similarly. Furthermore, NN-N was the second-best performing classifier and exhibited similar behaviors as the remaining classifiers based on neural networks (NN-M and NN-W). The classifiers that provided high precision rates could better classify healthy subjects. In contrast, those with high sensitivity rates could correctly classify patients with neurodegenerative problems. Figure 6: Confusion matrices of SVM-L and NN-M when normalized distances were used as feature vectors. The diagonal cells show the correctly classified observations (accuracies), whereas the off-diagonal cells show the incorrectly classified ones. The far-right column of each matrix shows the precision or positive predictive value (green) and the false discovery rate (red). The bottom row shows the sensitivity or true positive rate (green) and the false negative rate (red). Class 0 and class 1 represent healthy subjects and sick patients, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Norm. Distances + Angles**} \\ \hline
**Classifier** & **Accuracy** & **Sensitivity** & **Precision** \\  & **\%** & **\%** & **\%** \\ \hline DT & 81.0 & 100 & 76.5 \\ KNN & 90.5 & 84.6 & 100 \\ SVM-L & 85.7 & 76.9 & 100 \\
**SVM-Q** & **95.2** & **92.3** & **100** \\ SVM-C & 90.5 & 84.6 & 100 \\ NN-N & 90.5 & 100 & 86.7 \\ NN-M & 76.2 & 100 & 72.2 \\ NN-W & 85.7 & 100 & 81.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results when the combination of normalized distances (\(D_{d,j}\)) and angles (\(A_{a,j}\)) was used as a feature vector. The classifier providing better performance is highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Angles**} & \multicolumn{3}{c}{**Angles + Vel. + Acc.**} \\ \hline
**Classifier** & **Accuracy** & **Sensitivity** & **Precision** & **Accuracy** & **Sensitivity** & **Precision** \\  & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline DT & 57.1 & 76.9 & 62.5 & 71.4 & **92.3** & 70.6 \\ KNN & 76.2 & 76.9 & 83.3 & 66.7 & 61.5 & 80.0 \\ SVM-L & 81.0 & 76.9 & 90.9 & **85.7** & 84.6 & 91.7 \\ SVM-Q & **85.7** & **84.6** & **91.7** & 76.2 & 69.2 & 90.0 \\ SVM-C & 81.0 & 76.9 & 90.9 & 81.0 & 76.9 & 90.9 \\ NN-N & 66.7 & 76.9 & 71.4 & 66.7 & 46.2 & **100** \\ NN-M & 81.0 & 84.6 & 84.6 & 66,7 & 46.2 & **100** \\ NN-W & 76.2 & 76.9 & 83.3 & 71.4 & 53.8 & **100** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results when angles (\(A_{a,j}\)) and angles plus velocity (\(A^{\prime}_{a,i}\)) and acceleration (\(A^{\prime\prime}_{a,j}\)) were used as feature vectors. The maximum percentages are highlighted in bold. Figure 7: Confusion matrices of SVM-Q and NN-W when angles and angles plus velocity and acceleration were used as feature vectors, respectively. The diagonal cells show the correctly classified observations (accuracies), whereas the off-diagonal cells show the incorrectly classified ones. The far-right column of each matrix shows the precision or positive predictive value (green) and the false discovery rate (red). The bottom row shows the sensitivity or true positive rate (green) and the false negative rate (red). Class 0 and class 1 represent healthy subjects and sick patients, respectively.