## 4 Conclusions  
Three subsets of predictors derived from one-year CKD data were used to train the three machine learning models. The models' efficacy in predicting patients at risk of CKD in one year using various subsets predictors was similar across all models. Without creatinine data, the XGBoost classifier performs the best in all subsets (Subset A: AUC, 0.90; subset B: AUC, 0.92; Subset C: AUC, 0.93). More importantly, adding creatinine increased the generalization performance of all the models at an accuracy of 99% and AUC of 1.0. However, additional extensive modeling utilizing domain expertise and traditional approaches is still required to guarantee that the findings are interpretable.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122312001/s1](https://www.mdpi.com/article/10.3390/app122312001/s1)]([https://www.mdpi.com/article/10.3390/app122312001/s1](https://www.mdpi.com/article/10.3390/app122312001/s1)), Figure S1: Random forest classifier tree for Subset A: age, gender, and Tg/HDL-C ratio; Figure S3: Random forest classifier tree for Subset A: age, gender, and TG/HDL-C ratio; Figure S4: Random forest classifier tree for Subset A: age, gender, and VAI; Figure S4: Random forest classifier tree for Subset A: age, gender, VAI, and Creatinine.  
Figure 4: Shows model comparison based on the subset C: age, gender, visceral adiposity index and creatinine; (**A**) Receiver operating characteristic (ROC) curve and Precision-recall curve of the models using subset C; (**B**) confusion matrix of XGBoost classifier using subset C.  
**Author Contributions:** Conceptualization, A.W.J., C.-Y.H. and K.-C.C.: methodology, A.W.J.; software, A.W.J., A.N.S.B. and K.B.; validation, A.W.J., C.-Y.H. and K.-C.C.; formal analysis, A.W.J.; investigation, A.W.J.; resources, C.-Y.H. and K.-C.C.; data curation, A.W.J.; writing--original draft preparation, A.W.J. and A.N.S.B.; writing--review and editing, A.N.S.B., K.B., C.-Y.H. and K.-C.C.; supervision, C.-Y.H. and K.-C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Mei Jau (M.J.) Health Management Institute (Authorization code: MJHRF20170001A).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data that support the findings of this study are available from Mei Jau (M.J.) Health Management Institute but limited for research purpose only. The data are not publicly available due to privacy/ethical restriction. Data are available from the authors upon reasonable request and with permission of M.J. Health Management Institute.  
**Acknowledgments:** The authors thank M.J. Health Research Foundation group in Taiwan for providing the data and [PERSON] of National Taipei University of Nursing and Health Science for proofreading the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.