## 4 Results Analysis and Discussion
### Scenario 2  
In this case, the model training and testing are performed using the K-fold cross-validation criteria, where the value for K is selected as 10. K-fold cross-validation is also a standard model training and testing criteria previously used in different studies [15; 29; 44; 45]. Figure 6 shows the MAE analysis, providing insights into the performance of ML models for detecting abnormal ECG patterns using ECG signal data. Each model's MAE value reflects itss performance, with lower numbers indicating higher performance. Among the models, the NB model has the lowest MAE of 0.054, followed by the MLP model at 0.062. Both methods produce slightly better CDK predictions from ECG readings. In contrast, the SVM model has the greatest MAE of 0.132, suggesting less accurate predictions than the other models. The KNN model has a significantly higher MAE of 0.07. Decision tree-based models, such as J48 and LADTree, perform relatively well, with MAE values of 0.068 and 0.069.  
Figure 7 shows the KS values for each ML model used to detect abnormal ECG patterns, highlighting the agreement between predicted and actual results while accounting for chance agreement. The NB model had the best level of agreement across the models, with  
Figure 5: Comparison of Machine Learning Model Accuracy in abnormal ECG patterns Using the Percentage Splitting Criteria.  
Figure 6: Mean Absolute Error Analysis of Abnormal ECG Patterns on Employed Machine Learning Models Using ECG Signal Data with K-fold Cross-Validation.  
a Kappa score of 0.477, indicating significant agreement above chance and confirming its usefulness in predicting CDK outcomes. Following closely, the LADTree model has a Kappa score of 0.467, indicating a moderate to significant level of agreement over chance. The MLP and J48 models also have reasonable agreement with KS (0.446 and 0.453, respectively). In contrast, the SVM and KNN models had lower levels of agreement, with Kappa values of 0.297 and 0.326, respectively, indicating room for improvement in their prediction ability. Overall, the KS analysis emphasizes the different levels of agreement between the models' predictions and actual abnormal ECG patterns, providing valuable insights into their ability to detect these patterns.  
Figure 8 shows the performance measures for ML models used to detect abnormal ECG patterns, including recall, ROC area, and PRC area. Among the models, the NB model has the best overall performance, with notable scores in recall (0.662), ROCA (0.789), and PRCA (0.62), indicating its effectiveness in correctly identifying positive CKD cases, distinguishing between positive and negative instances, and maintaining a balance between precision and recall. The LADTree model comes in second place, with recall, ROCA, and PRCA scores of 0.673, 0.793, and 0.602, respectively. Meanwhile, the MLP, J48, and SVM models show moderate performance in detecting abnormal ECG patterns and distinguishing between classes, while the KNN model lags with lower scores across all metrics, indicating relatively weaker performance in detecting these patterns.  
Figure 9 shows the accuracy values of the applied ML models in detecting abnormal ECG patterns using ECG signal data. Among the models, the LADTree model has the best  
Figure 8: Performance Measures (Recall, ROCA, PRCA) of Machine Learning Models for Predicting Abnormal ECG Patterns using K-Fold Cross-Validation.  
Figure 7: Kappa Statistic (KS) Values for Employed Machine Learning Models Used in Predicting Abnormal ECG Patterns Using K-Fold Cross-Validation.  
accuracy of 67.257%, followed by the MLP model at 66.372%. These findings show that both models have rather better prediction ability in reliably diagnosing cases of CKD using ECG data. In contrast, the KNN model has the lowest accuracy of 56.195%, indicating that it has worse predictive ability than other models. The SVM and J48 models have accuracies of 63.053% and 65.255%, suggesting reasonable performance levels. The NB model has an accuracy of 66.15%, which is competitive with MLP and J48.