## 5 Conclusions  
This study aimed to enhance the prediction of abnormal ECG patterns using ECG signal data and ML models. It compared the LADTree model with several well-known ML algorithms, including MLP, KNN, SVM, NB, and J48. Comprehensive testing and analysis provided valuable insights into the performance of these models for detecting abnormal ECG patterns. Our results constantly showed that the LADTree model performed better in terms of mistake rate and accuracy evaluation measures. Notably, the LADTree model continuously performed better on measures including MAE, KS, recall, ROCA, PRCA, and overall accuracy. The study also emphasized how crucial feature selection is to improving machine learning models' capacity for prediction. All things considered, these results highlight the promise of the LADTree model and underscore the significance of effective feature selection in optimizing the detection of abnormal ECG patterns using ECG signal data. Through the use of many feature selection methods, such as Particle Swarm Optimization, Best First Search, and Harmony Search, we were able to find non-redundant and relevant features for CKD prediction using ECG data. Overall, this study's findings demonstrate the potential of machine learning models, especially the LADTree model, to support the early identification and diagnosis of CKD using data from ECG signals. These findings have important ramifications for the creation of sophisticated diagnostic instruments and individualized healthcare programs meant to enhance CKD management and lower the rates of morbidity and death connected with the disease. To improve the clinical value and dependability of ML models, additional research in this field may concentrate on improving feature selection techniques, maximizing model parameters, and verifying the models' performance on bigger and more varied datasets. Future research for this study might include investigating ensemble approaches that mix different ML models, including the LADTree model, to potentially improve prediction accuracy. Furthermore, using deep learning for CKD prediction using ECG data shows potential. It would be advantageous to conduct larger-scale investigations with various datasets, validate model robustness, and improve interpretability using approaches such as attention processes. Furthermore, including real-time monitoring capabilities in created models might allow for early identification and continuous monitoring of CKD development, leading to improved diagnosis and management of this common medical illness. Conceptualization, B.K. and M.B. ; methodology, B.K. ; software, M.B. ; validation, B.K. and M.B. ; formal analysis, M.B. ; investigation, B.K. ; resources, M.B. ; data curation, B.K. ; writing--original draft preparation, B.K. ; writing--review and editing, M.B. ; visualization, B.K. ; supervision, B.K. ; project administration, M.B. ; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. This research work was funded by Institutional Fund Projects under grant no. (GPIP: 778-611-2024). The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. The data used in this study is online available at: [[https://physionet.org/about/database/](https://physionet.org/about/database/)]([https://physionet.org/about/database/](https://physionet.org/about/database/)) (accessed on 30 March 2024). This research was supported by the Institutional Fund Projects under grant number GPIP: 778-611-2024. The authors express their sincere gratitude to the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia, for their invaluable technical and financial support.