# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 4 Experiment and Analysis
### An In-Depth Analysis Based on the Power and Relative Features of EEG  
In this section, we conduct a semantic analysis of stroke disease in the elderly using the power value of EEG based on the C4.5 algorithm among decision trees, a representative classification and prediction model of machine learning. In the first experiment, as a semantic analysis of the rules on predicting stroke conditions in older people, we randomly extracted and learned the power values of 2352 sets of data from both normal elderly and stroke patients, and we conducted testing with 588 data sets for normal elderly and stroke patients who did not participate in the learning. This section describes the experimental results of the C4.5 DT algorithm in the fourth experiment based on Equation (1) in Section 4.3.1. In the C4.5 DT algorithm, when 80% of train data and 20% of test data were applied, satisfactory prediction accuracy (86.39%), and stable performance of 86.4% for recall and precision was confirmed. Figure 5 shows a decision tree for predicting stroke conditions in the elderly with only eight attributes by applying the power value as CFS. In the construction of the decision trees, we showed that only eight of the 66 power value attributes defined by the system can be used to accurately classify and predict normal elderly and stroke patients. Here, the number in the leaf node is the corresponding class by learning data, which means the exact number of predictions and the number of incorrect predictions. Fourteen rules can be obtained from Figure 5. Summarizing Figure 5, the Fz channel values from the power values are analyzed as the main attributes. This was analyzed and confirmed by experiments indicating that the precursors for stroke disease \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Train (67)/** & \multicolumn{2}{c}{**Train (80)/**} & \multicolumn{2}{c}{**5-Fold CV**} & \multicolumn{2}{c}{**10-Fold CV**} & \multicolumn{2}{c}{**20-Fold CV**} \\  & **Test (33)** & \multicolumn{2}{c}{**Test (20)**} & & & & & & & \\ \cline{2-11}  & **Recall** & **Prec.** & **Recall** & **Prec.** & **Recall** & **Prec.** & **Recall** & **Prec.** & **Recall** & **Prec.** \\ \hline RandomForest & 89.2 & 89.2 & 89.8 & 89.8 & 89.7 & 89.9 & 90.5 & 90.6 & 90.1 & 90.3 \\ C4.5 DT & 81.4 & 81.4 & 83.6 & 83.6 & 82.5 & 82.6 & 82.9 & 83.0 & 82.4 & 82.6 \\ C5.0 DT & 77.0 & 77.4 & 78.8 & 78.9 & 77.7 & 77.7 & 78.6 & 78.9 & 78.8 & 78.9 \\ Naive Bayes & 73.0 & 73.5 & 73.7 & 74.3 & 73.5 & 74.2 & 73.4 & 74.1 & 73.4 & 74.0 \\ LR & 78.5 & 78.5 & 79.1 & 79.1 & 78.7 & 78.7 & 78.7 & 78.6 & 78.6 \\ MLP(ANN) & 84.4 & 84.4 & 86.4 & 86.5 & 86.1 & 86.1 & 87.1 & 86.9 & 87.1 \\ SVM & 74.0 & 74.6 & 74.6 & 74.9 & 73.7 & 74.3 & 74.5 & 74.8 & 74.2 & 74.6 \\ ADTree & 79.9 & 80.0 & 79.2 & 79.5 & 79.2 & 79.4 & 79.5 & 79.6 & 79.1 & 79.4 \\ C\&RT & 80.1 & 80.1 & 80.3 & 80.3 & 80.2 & 80.3 & 80.5 & 80.6 & 80.4 & 80.5 \\ QUEST & 73.9 & 73.9 & 73.9 & 73.9 & 73.4 & 73.7 & 73.8 & 73.9 & 73.5 & 73.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recall and Precision (%) for each algorithm using EEG relative values. in older people were significant in the frontal lobe of the brain. Further, as shown in previous studies, attribute values such as \(\theta\)to\(\beta(\theta/\beta)\), PRI(\(\delta+\theta)/(\alpha+\beta)\), and theta (\(\theta\)) are well identifiable and predictable for the characteristics of stroke patient brain waves. Figure 6 shows the experimental results of extracting eight attributes from the power values through the CFS and applying 10-fold CV. When compared to Figure 5, these experimental results suggest that stroke precursors in older people can be accurately predicted and determined by only seven rules. We showed the improvement in the prediction accuracy of stroke disease to 86.96%.