## 4 Results Analysis and Discussion
### Discussion  
The study focuses on detecting abnormal ECG patterns using ECG data, specifically evaluating the performance of the LADTree model compared to other standard ML models. The analysis includes two scenarios: scenario 1, which uses percentage splitting for training and testing, and scenario 2, which uses K-fold cross-validation. In scenario 1, the models' prediction ability is assessed using MAE and KS. While the NB model has the lowest MAE, showing stronger prediction accuracy, the LADTree model outperforms KS, demonstrating superior classification accuracy. Moreover, the LADTree model consistently excels in the recall, ROC, and PRC metrics, showcasing its ability to effectively detect abnormal ECG patterns while maintaining a balance of accuracy and recall. Although the NB and MLP models also show competitive MAE values, the LADTree model maintains strong performance across various metrics. The NB model demonstrates notable efficiency in predicting CKD based on agreement with actual results. However, the LADTree model stands out in terms of recall, ROC area, and PRC, underscoring its suitability for identifying abnormal ECG patterns. In terms of accuracy, the LADTree model again proves to be the top performer, demonstrating its reliability in diagnosing conditions related to abnormal ECG data. Furthermore, the superior performance of the LADTree model is evident. Its robustness to noise and outliers, combined with its capability to handle both numerical and categorical data, makes it particularly effective. This allows LADTree to capture complex relationships within ECG signals and reliably aid in the identification of abnormal patterns. Furthermore, the hierarchical structure of LADTree improves interpretable decision-making, allowing for the discovery of significant variables critical for CKD prediction. However, greater investigation into how LADTree's decision-making process varies from other models, as well as its computing efficiency, would give a more complete explanation of its superiority. Overall, while the research offers valuable insights, addressing these underlying aspects and concerns will enhance our understanding of the findings and underscore the LADTree model's advantages in detecting abnormal ECG patterns. Comparing LADTree's performance against that of J48, another decision tree, is another factor. The decision tree algorithms J48 and LADTree are renowned for their interoper  
Figure 9: Accuracy Values of Employed Machine Learning Models for Predicting Abnormal ECG Patterns Using Electrocardiogram (ECG) Signal Data Based on the K-Fold Cross-Validation Criteria. ability, which is essential in medical applications. Nonetheless, the observed inequalities in performance can be explained by significant variances in their underlying structures and processes. Combining boosting with decision trees, the LADTree is an alternating decision tree type that can handle more complicated connections and provide predictions with a better degree of accuracy. It combines many trees to capture a wider variety of patterns in the data, which is especially useful for complex datasets like ECG signals used to forecast chronic kidney disease. J48, an algorithmic implementation of C4.5, on the other hand, constructs a single decision tree according to criteria related to information gain or gain ratio. J48 performs relatively poorly when dealing with the increased complexity and noise seen in medical data, even though it is successful with simpler datasets. Because of its sturdy construction, the LADTree is better able to handle noise and outliers, which enhances prediction accuracy and dependability. The study's limitations derive basically from its dependence on a single dataset, the MIT-BIH Arrhythmia database, which may not completely represent the various ECG patterns found in the general population with CKD. As a result, the findings' generalizability may be restricted since the model's performance varies among different ECG datasets or populations.