## 3 Results
### Feature-Based Models  
For feature-based models, XGB and DT performed the best with an accuracy of 0.99 and F\({}_{1}\)-score 0.99 for both models (Table 3). After evaluating the feature-based model performance results, features were explored for each of the models to determine importance of features (Figure 3), as well as importance of modalities. As can be seen in Table 4, the Skin Conductance Level (SCL) max feature from EDA was the feature of the highest importance for six of the seven models. This result led to EDA being defined as the most important modality for anxiety detection, and more specifically SCL max. The second most important feature was BVP max and ranked number 2. In Table 4, the columns categorize and quantify the contributions of different physiological signals and their specific features to the model's performance.  
The weighted average is a crucial metric, calculated by squaring each feature's importance score and dividing this total by the sum of the top seven feature importance scores, expressed as given by Equation (3):  
\[W=\frac{\sum_{i=1}^{n}X_{i}^{2}}{\sum_{i=1}^{n}X_{i}} \tag{3}\]  
\[W=weighted\;average\]  
\[n=number\;of\;features\]  
\[X_{i}=Feature\;importance\]  
where \(X_{i}\) represents the \(i\)th feature importance value listed in Table 4 for a given modality, and \(n\) represents the total number of feature importance scores listed in Table 4. This method emphasizes the impact of more influential features by assigning greater weight to higher scores, providing a nuanced view of how each feature impacts the model's ability to detect anxiety. This table format effectively showcases which physiological measurements are most impactful under varying conditions, highlighting the features that consistently influence anxiety detection outcomes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Feature**} & \multirow{2}{*}{
\begin{tabular}{c} **Weighted** \\ **Average** \\ \end{tabular} } & \multirow{2}{*}{**DT**} & \multirow{2}{*}{**kNN**} & \multirow{2}{*}{**LDA**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**AB**} \\ \hline \multirow{4}{*}{ACC} & ACC\({}_{\text{x\,min}}\) & & 0.03 & & & & & 0.07 & 0.12 \\ \cline{2-8}  & ACC\({}_{\text{x\,std}}\) & 0.06 & & & & 0.05 & & & \\ \cline{2-8}  & ACC\({}_{\text{net\,w\,min}}\) & & 0.04 & & & 0.06 & & & \\ \cline{2-8}  & ACC\({}_{\text{net\,w\,max}}\) & & & & & 0.05 & & & \\ \hline \multirow{3}{*}{BVP} & BVP\({}_{\text{max}}\) & & & 0.39 & & & & & \\ \cline{2-8}  & BVP\({}_{\text{min}}\) & 0.15 & & 0.38 & & & & & \\ \cline{2-8}  & BVP\({}_{\text{std}}\) & & & 0.14 & & & & & \\ \hline \multirow{4}{*}{ECG} & ECG\({}_{\text{ppm}}\) & & 0.09 & 0.14 & 0.07 & 0.05 & 0.26 \\ \cline{2-8}  & ECG\({}_{\text{pmn50}}\) & & & & 0.05 & & & \\ \cline{2-8}  & ECG\({}_{\text{rmssd}}\) & 0.10 & & & & & & \\ \cline{2-8}  & ECG\({}_{\text{sddn}}\) & & & 0.11 & & & & & \\ \cline{2-8}  & ECG\({}_{\text{sdsd}}\) & & & 0.03 & & & & & \\ \cline{2-8}  & ECG\({}_{\text{sd}}\) & & 0.13 & 0.06 & 0.04 & & 0.06 & 0.10 \\ \hline \multirow{4}{*}{EDA} & EDA\({}_{\text{SCL\,max}}\) & & 0.43 & 0.15 & 0.15 & 0.21 & 0.15 & 0.30 \\ \cline{2-8}  & EDA\({}_{\text{SCR\,max}}\) & & & 0.06 & & & 0.06 & 0.02 \\ \cline{2-8}  & EDA\({}_{\text{SCR\,sdt}}\) & & & 0.06 & & & 0.06 & & \\ \cline{2-8}  & EDA\({}_{\text{std}}\) & & & 0.08 & & & 0.08 & & \\ \cline{2-8}  & Resp\({}_{\text{C\,Exhal\,std}}\) & & & & 0.04 & & & 0.02 \\ \cline{2-8}  & Resp\({}_{\text{C\,Exhal\,std}}\) & & & 0.08 & 0.10 & & & 0.08 & \\ \cline{2-8}  & Resp\({}_{\text{C\,Exhal\,std}}\) & & & 0.06 & & & 0.09 & 0.06 & \\ \hline TEMP & TEMP\({}_{\text{mean}}\) & 0.08 & 0.11 & & & 0.05 & & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature importance for self-learned feature-based models on the WESAD dataset.