# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## 4 Experiments and Analysis
### Experiment and Analysis Based on Machine Learning
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NumberContents** & **Attributes** & **Description** \\ \hline
1 & BFL\_Mmax & The value from the lowest point of the negative peak of the left biceps femoris to the positive peak. \\ \hline
2 & BFR\_Mmax & The value from the lowest point of the negative peak of the right biceps femoris to the positive peak. \\ \hline
3 & LGL\_Mmax & The value from the lowest point of the negative peak of the left gastrocnemius \\ \hline
4 & LGR\_Mmax & The value from the lowest point of the negative peak of the right gastrocnemius \\  & & muscle to the positive peak. \\ \hline
5-8 & BFL\_PositivePeak, & The maximum value of the positive peak based on 0 of biceps femoris/ gastrocnemius \\  & LGR\_PositivePeak & muscle on the left and right. \\ \hline
9-12 & BFL\_NegativePeak, & \\  & BFR\_NegativePeak, & \\  & LGL\_NegativePeak, & \\  & LGR\_NegativePeak, & \\ \hline
13–16 & BFL\_5,PPMean, & \\  & BFR\_5,PPMean, & \\  & LGL\_5,PPMean, & \\  & LGL\_5,PPMean, & \\  & PPMean & \\ \hline
17–20 & BFL\_10,PPMean, & \\  & BFR\_10,PPMean, & \\  & LGL\_10,PPMean, & \\  & LGR\_10,PPMean, & \\ \hline
21–24 & BFL\_5 PPISD, & \\  & LGL\_5,PPPSD, & \\  & LGL\_5,PPSD, & \\  & LGR\_5,PPSD, & \\ \hline
25–28 & BFL\_10 PPISD, & \\  & LGR\_10 PPISD, & \\  & LGR\_10 PPISD, & \\ \hline
29 & Class Labeling & Normal or Stroke \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed Descriptions of Newly Defined and Extracted EMG Attributes. In the second experiment, stroke prediction performance was verified by applying quartiles to the 28 attributes. Medical data (EHR) are typically eliminated using a statistical method of quartiles for missing and out-of-range values. In this experiment, the stroke disease prediction was applied to remove the corresponding data rows by quartile, and the experimental results presented in Table 3 below were obtained. As with the first experiment, the Random Forest algorithm showed the best performance, with predicted accuracies of 85.86% and 85.84% at 10-fold CV and 20-fold CV, respectively. The third experiment compared the accuracy of the prediction model with data that applied the method of selecting quartiles and correlation-based feature selection (CFS) on the original data (see Table 4). CFS is a method of reducing the size of a data set by eliminating duplicate or unrelated features for classification. In addition, for data classification that involves finding the optimal set of features suitable for the goal, the probability distribution will be similar to or even higher than the probability distribution obtained using all the features. For this experiment, not all of the extracted features were used; instead, the Hall [54] method was used to select a subset of features. [PERSON]'s method calculates conditional probabilities using entropy for the attribute value and the best first search value, \(Y\), as well as Pearson's correlation coefficient between the target class and the attributes. First, the entropy was calculated for any attribute \(Y\) as shown in Equation (1) to obtain information benefits for each attribute [54; 55]. \[H(Y)=-\sum_{y\in Y}p(y)log_{2}(p(y)) \tag{1}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods Data Sets** & **Train (70)/ Test (30)** & **Train (80)/ Test (20)** & **5-Fold CV** & **10-Fold CV** & **20-Fold CV** \\ \hline C4.5 Decision Tree & 80.08 & 80.58 & 81.90 & 82.20 & 82.27 \\ C5.0 Decision Tree & 82.48 & 82.65 & 83.11 & 83.15 & 83.18 \\ Naive Bayes & 69.25 & 69.14 & 69.58 & 69.60 & 69.58 \\ Logistic Regression & 71.08 & 70.90 & 71.13 & 71.16 & 71.13 \\ (LR) & 76.70 & 77.73 & 77.62 & 77.53 & 77.68 \\ ANN (MLP) & 85.94 & 85.88 & 86.52 & 86.89 & 86.88 \\ C\&RT & 80.59 & 80.56 & 80.72 & 80.89 & 80.78 \\ CHAID & 80.42 & 80.37 & 80.67 & 80.91 & 80.98 \\ Two-Class SVM & 74.01 & 73.98 & 74.24 & 74.67 & 74.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction accuracy of stroke with quartile and CFS (%).