## 4 Results Analysis and Discussion
### Scenario 1  
In this case, the model training and testing are performed using the percentage splitting criteria. The data is split into 70% and 30% for training and testing respectively. Figure 2 shows the MAE values for ML models used to detect abnormal ECG patterns from ECG signal data. Notably, the NB model has the lowest MAE of 0.048, showing higher predictive accuracy than the other models. In contrast, the SVM model has the greatest MAE (0.132), signifying substantially inferior predictive ability. The MLP and LADTree models have modest MAE values of 0.057 and 0.059, respectively, but the KNN and J48 decision tree models have MAE values of 0.073 and 0.065. Figure 3 shows the KS values for ML models used to predict abnormal ECG patterns from ECG signal data. The Kappa Statistic measures inter-rater agreement or classification accuracy for categorical outcomes, with values closer to one indicating higher agreement or classification performance. In this scenario, the LADTree model has the highest KS value (0.634), indicating the best agreement between projected and observed classifications among the models tested. Following closely is the MLP model, which has a KS score of 0.585, showing a high level of agreement. The NB model also has a comparatively high KS value of 0.553, which indicates strong classification accuracy. In contrast, the KNN and J48 decision tree models have KS values of 0.315 and 0.466, respectively, showing moderate agreement. Notably, the SVM model has the lowest KS value of 0.314, signifying lower classification performance than the other models. Overall, the analysis indicates that the LADTree, MLP, and NB models beat others in terms of classification accuracy for CKD prediction, however, the SVM model performs poorly based on Kappa Statistic values. Figure 2: Comparison of Mean Absolute Error (MAE) Values for Machine Learning Models in Predicting Chronic Kidney Disease (CKD) from Electrocardiogram (ECG) Signal Data Using Percentage Splitting Criteria. Figure 4 shows that the LADTree model consistently surpasses the other models in all three metrics: recall, ROC area, and PRC area. With a recall of 0.779, the LADTree model outperforms all other models in detecting positive cases of renal disease. Furthermore, its ROC area of 0.864 indicates a better classification capacity than the other models. Furthermore, the LADTree model obtains a PRC area of 0.75, demonstrating an excellent balance of accuracy and recall. This overall result implies that the LADTree model is well-suited to the task of predicting renal illness, with high accuracy in detecting positive instances and good precision. While competing models, such as MLP and NB, perform competitively in some measures, the LADTree model consistently outperforms all tested criteria. Based on the data reported, the LADTree model should be considered the leading choice for renal disease prediction. The accuracy analysis of the applied machine learning models provides useful information about their efficacy in predicting renal illness is presented in Figure 5. The LADTree model outperforms the other models, with an accuracy of 77.942%, suggesting that it can accurately anticipate the outcome in about 78% of situations. This demonstrates the LADTree algorithm's ability to appropriately categorize occurrences of renal illness. The MLP model follows closely behind with an accuracy of 75%, demonstrating its outstanding overall performance in classification tasks. The NB model also performs competitively, with an accuracy of 71.323%, indicating its usefulness in medical diagnosing tasks. How  
Figure 4: Comparison of Performance Metrics Across Employed Machine Learning Models for Abnormal ECG Patterns Prediction using the Percentage Splitting Criteria. Figure 3: Comparison of Kappa Statistic (KS) Values for Employed Models in Predicting Abnormal ECG Patterns Using the Percentage Splitting Criteria. ever, other models, such as SVM, J48, and KNN, have significantly lesser accuracies of 65.441%, 64.706%, and 54.412%.