## 3 Research Design and Procedure
### ML Models, Training, and Performance Evaluation  
This study seeks to enhance the accuracy of detecting abnormal ECG patterns using the LADTree model and ECG signal data. Table 3 compares the derived prediction models to many traditional machine learning methods. There are two unique approaches to training and evaluation. First, the percentage splitting strategy is utilized, with 70% of the dataset dedicated to training each model and the remaining 30% to testing. Second, a K-fold cross-validation approach is used, with K equal to 10. This approach enables a thorough evaluation of the model's performance by systematically partitioning the dataset into subsets for training and testing, revealing insights into their prediction capabilities under various validation techniques. While Table 4 presents the list of benchmarked models used in this study.  
Standard measures like recall, the Classification Accuracy (CA) [23; 24], the Receiver Operating Characteristic (ROC) area, the Precision-Recall Curve (PRC) area [25; 26], the Kappa Statistics (KS) [27], and the Mean Absolute Error (MAE) [28; 29] are used to evaluate the performance. The four values in the confusion matrix produced by the machine learning model are True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN). These metrics are obtained from this matrix.  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[CA=\frac{TP+TN}{TP+FP+TN+FN} \tag{12}\]  
\[KS=\frac{Po-Pe}{1-Pe} \tag{13}\]  
The percentage of situations in which the predicted and actual classifications match is shown by P\({}_{\text{o}}\) (Observed Agreement) in this equation, whereas the percentage of cases where agreement would be anticipated just by chance is indicated by Pe (anticipated Agreement by Chance).  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-x_{i}|}{n} \tag{14}\]  
In this case, N is the number of samples, \(x_{i}\) is the true (actual) label or value, and \({}_{I}\,y_{i}\) is the predicted label or value.  
**Receiver Operating Characteristic (ROC) Area:** The ROC area is calculated by utilizing the ROC curve to graph TPR vs. FPR at different decision thresholds. Often, software libraries or numerical integration are used to determine the ROC area.  
**Precision-Recall Curve (PRC) Area:** The PRC area is obtained by utilizing the PRC curve to graph Precision vs. Recall at different decision criteria. Software library tools or numerical integration are frequently used to compute the PRC area.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**ML Models** & **References** \\ \hline Multilayer Perceptron (MLP) & [30; 31; 32] \\ \hline K-Nearest Neighbor (KNN) & [31; 32; 33] \\ \hline Support Vector Machine (SVM) & [34; 35; 36] \\ \hline Naive Bayes (NB) & [37; 38] \\ \hline J48-Decision Tree (J48) & [29; 32; 39] \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of the Models Employed in this Study Compared with the Projected Model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Search Method** & **Selected Features** \\ \hline PSO Search & AH, BO, BN, BV, CZ, CI, DM, DK, DZ, DS, DO, EY, EM, EB, FT, FA, \\  & GO, GE, HR, HL, HT, IN, JD, JJ, JH, IB, YJ, JP, JO, IV, KS, KH, KY, LE, \\  & r\_wave, t\_interval, qrs\_duration = 37 \\ \hline Best First Search & AU, CI, DK, DD, DA, DZ, DN, EB, HR, HJ, IV, IN, IT, IH, JV, JB, JY, KS, \\  & LE, LG, q-t\_interval, heart\_rate, qrs\_duration, t\_interval, T = 25 \\ \hline \multirow{2}{*}{Harmony Search} & BN, BI, BY, DK, DB, EF, EB, EN, EM, FC, FB, FO, GR, HR, HN, IV, IJ, \\  & JO, JB, KS, KO, KU = 22 \\ \hline \multirow{2}{*}{Ending Operation} & BN, CI, DK, EB, LE, JO, JV, JY, IN, EM, DZ, IV, JB, KS, HR, \\  & qrs\_duration = 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of Selected Features using each Searching Method [21].