## 3 Results  
### Time-Domain Features' Classification Results  
The 12 features of the time domain with \(p\)-values \(<\) 0.05 were classified using the WEKA machine learning library using 10-fold cross-validation, and the average accuracy across all folds is reported for each of the classifiers. Table 13 shows the results of classifying the time-domain features with \(p\)-values \(<\) 0.05. The random forest classifier had the best results, with an accuracy of 83.22%.  
Other classification performance metrics are reported for different BAC classes in Table 14 for the random forest classifier.  
Figure 4 summarizes the confusion matrix performance of the random forest classifier on time-domain features with \(p\)-values \(<\) 0.05. The classifier showed high classification ability, with leading values in the diagonal of the confusion matrix.  
\begin{table}
\begin{tabular}{l c} \hline
**Classifier Type** & **Accuracy (\%)** \\ \hline
**Random Forest** & **83.22** \\ JRip & 80.20 \\ J48 & 78.86 \\ Decision Table & 74.16 \\ Naive Bayes & 48.66 \\ SMO (SVM in WEKA) & 41.28 \\ \hline \end{tabular}
\end{table}
Table 13: Classifiers ranked by accuracy for time-domain features with \(p\)-values \(<\) 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BAC = 0 & 0.942 & 0.049 & 0.803 & 0.942 & 0.867 & 0.968 \\ BAC = 0.05 & 0.625 & 0.039 & 0.714 & 0.625 & 0.667 & 0.855 \\ BAC = 0.12 & 0.807 & 0.054 & 0.780 & 0.807 & 0.793 & 0.909 \\ BAC = 0.2 & 0.632 & 0.035 & 0.727 & 0.632 & 0.676 & 0.836 \\ BAC = 0.3 & 0.937 & 0.032 & 0.945 & 0.937 & 0.941 & 0.979 \\ \hline Weighted Avg. & 0.932 & 0.040 & 0.830 & 0.832 & 0.829 & 0.929 \\ \hline \end{tabular}
\end{table}
Table 14: Random forest classifier performance metrics for different BAC Classes.  
Figure 5 visualizes the confusion matrix performance of the J48 classifier on frequency-domain features with \(p\)-values < 0.05. The classifier showed high classification ability, with leading values in the diagonal of the confusion matrix.  
Table 18 shows the model configuration for this J48 classifier in WEKA.  
\begin{table}
\begin{tabular}{l l} \hline
**Classifier Type** & **Accuracy** \\ \hline
**J48** & **82.21\%** \\ Random Forest & 79.53\% \\ JRip & 77.18\% \\ Decision Table & 74.83\% \\ Naive Bayes & 48.99\% \\ SMO (SVM in WEKA) & 43.29\% \\ \hline \end{tabular}
\end{table}
Table 16: Classifier accuracy comparison on frequency-domain features with \(p\)-values < 0.05.  
Figure 5: J48 classifier confusion matrix performance on frequency-domain features with \(p\)-values < 0.05.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BAC = 0 & 0.885 & 0.061 & 0.754 & 0.885 & 0.814 & 0.913 \\ BAC = 0.05 & 0.650 & 0.027 & 0.788 & 0.650 & 0.712 & 0.904 \\ BAC = 0.12 & 0.842 & 0.079 & 0.716 & 0.842 & 0.774 & 0.874 \\ BAC = 0.2 & 0.605 & 0.023 & 0.793 & 0.605 & 0.687 & 0.852 \\ BAC = 0.3 & 0.919 & 0.032 & 0.944 & 0.919 & 0.932 & 0.958 \\ \hline Weighted Avg. & 0.822 & 0.044 & 0.827 & 0.822 & 0.820 & 0.913 \\ \hline \end{tabular}
\end{table}
Table 17: J48 classifier performance metrics for different BAC classes.