|
1.Abbate, M., Mascaró, C. M., Montemayor, S., Casares, M., Gómez, C., Ugarriza, L., Tejada, S., Abete, I., Zulet, M. A., Sureda, A., Martínez, J. A., & Tur, J. A. (2021). Non-Alcoholic Fatty Liver Disease Is Associated with Kidney Glomerular Hyperfiltration in Adults with Metabolic Syndrome. Journal of Clinical Medicine, 10(8), 1717. https://doi.org/10.3390/jcm10081717 2.Abutaleb, N. (2007). Why we should sub-divide CKD stage 3 into early (3a) and late (3b) components. Nephrology Dialysis Transplantation, 22(9), 2728–2729. https://doi.org/10.1093/ndt/gfm349 3.Aguilar, M., Bhuket, T., Torres, S., Liu, B., & Wong, R. J. (2015). Prevalence of the metabolic syndrome in the United States, 2003-2012. JAMA, 313(19), 1973–1974. https://doi.org/10.1001/jama.2015.4260 4.Ahn, H. S., Kim, J. H., Jeong, H., Yu, J., Yeom, J., Song, S. H., Kim, S. S., Kim, I. J., & Kim, K. (2020). Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction. International Journal of Molecular Sciences, 21(12), 4236. https://doi.org/10.3390/ijms21124236 5.Akahane, T., Akahane, M., Namisaki, T., Kaji, K., Moriya, K., Kawaratani, H., Takaya, H., Sawada, Y., Shimozato, N., Fujinaga, Y., Furukawa, M., Kitagawa, K., Ozutsumi, T., Tsuji, Y., Kaya, D., Mitoro, A., & Yoshiji, H. (2020). Association between Non-Alcoholic Fatty Liver Disease and Chronic Kidney Disease: A Cross-Sectional Study. Journal of Clinical Medicine, 9(6), 1635. https://doi.org/10.3390/jcm9061635 6.Alberti, K. G., & Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetic Medicine, 15(7), 539–553. https://doi.org/10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S 7.Alebiosu, C. O., & Ayodele, O. E. (2005). The global burden of chronic kidney disease and the way forward. Ethnicity Disease, 15(3), 418–423. 8.Alghamdi, M., Al-Mallah, M., Keteyian, S., Brawner, C., Ehrman, J., & Sakr, S. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLOS ONE, 12(7), e0179805. https://doi.org/10.1371/journal.pone.0179805 9.Alkaim, A. F., & Al-Janabi, S. (2019, May). Multi objectives optimization to gas flaring reduction from oil production. In Proceedings of the International Conference on Big Data and Networks Technologies, Leuven, Belgium, pp. 117–139. https://doi.org/10.1007/978-3-030-23672-4_10 10.Al-Sarem, M., Saeed, F., Alkhammash, E. H., & Alghamdi, N. S. (2021). An aggregated mutual information based feature selection with machine learning methods for enhancing iot botnet attack detection. Sensors, 22(1), 185. https://doi.org/10.3390/s22010185 11.Alshboul, O., Shehadeh, A., Almasabha, G., & Almuflih, A. S. (2022). Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction. Sustainability, 14(11), 6651. https://doi.org/10.3390/su14116651 12.Amasyali, M. F., & Ersoy, O. (2013). A comparative review of regression ensembles on drug design datasets. Turkish Journal of Electrical Engineering and Computer Sciences, 21(2), 586–602. https://doi.org/10.3906/elk-1102-1033. 13.Anavekar, N. S., McMurray, J. J., Velazquez, E. J., Solomon, S. D., Kober, L., Rouleau, J. L., White, H. D., Nordlander, R., Maggioni, A., Dickstein, K., Zelenkofske, S., Leimberger, J. D., Califf, R. M., & Pfeffer, M. A. (2004). Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. The New England Journal of Medicine, 351(13), 1285–1295. https://doi.org/10.1056/NEJMoa041365 14.Arase, Y., Suzuki, F., Kobayashi, M., Suzuki, Y., Kawamura, Y., Matsumoto, N., Akuta, N., Kobayashi, M., Sezaki, H., Saito, S., Hosaka, T., Ikeda, K., Kumada, H., Ohmoto, Y., Amakawa, K., Tsuji, H., Hsieh, S. D., Kato, K., Tanabe, M., Ogawa, K., … Kobayashi, T. (2011). The development of chronic kidney disease in Japanese patients with non-alcoholic fatty liver disease. Internal Medicine, 50(10), 1081–1087. https://doi.org/10.2169/internalmedicine.50.5043 15.Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249 16.Assiry, A., Alshahrani, S., Banji, D., Banji, O. J. F., Syed, N. K., & Alqahtani, S. S. (2022). Public Awareness of Chronic Kidney Disease in Jazan Province, Saudi Arabia-A Cross-Sectional Survey. Healthcare, 10(8), 1377. https://doi.org/10.3390/healthcare10081377 17.Atkinson, M. A., Pierce, C. B., Zack, R. M., Barletta, G. M., Yadin, O., Mentser, M., Warady, B. A., & Furth, S. L. (2010). Hemoglobin differences by race in children with CKD. American Journal of Kidney Diseases, 55(6), 1009–1017. https://doi.org/10.1053/j.ajkd.2009.12.040 18.Baek, S. D., Baek, C. H., Kim, J. S., Kim, S. M., Kim, J. H., & Kim, S. B. (2012). Does stage III chronic kidney disease always progress to end-stage renal disease? A ten-year follow-up study. Scandinavian Journal of Urology and Nephrology, 46(3), 232–238. https://doi.org/10.3109/00365599.2011.649045 19.Balkau, B., & Charles, M. A. (1999). Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabetic Medicine, 16(5), 442–443. https://doi.org/10.1046/j.1464-5491.1999.00059.x 20.Balogun, A. O., Basri, S., Capretz, L. F., Mahamad, S., Imam, A. A., Almomani, M. A., Adeyemo, V. E., & Kumar, G. (2021). An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction. Entropy, 23(10), 1274. https://doi.org/10.3390/e23101274 21.Baratta, F., D’Erasmo, L., Di Costanzo, A., Umbro, I., Pastori, D., Angelico, F., & Del Ben, M. (2022). Metabolic Syndrome but Not Fatty Liver-Associated Genetic Variants Correlates with Glomerular Renal Function Decline in Patients with Non-Alcoholic Fatty Liver Disease. Biomedicines, 10(3), 720. https://doi.org/10.3390/biomedicines10030720 22.Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357–365. https://doi.org/10.2307/2280041 23.Bienefeld, C., Kirchner, E., Vogt, A., & Kacmar, M. (2022). On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor. Lubricants, 10(4), 67. https://doi.org/10.3390/lubricants10040067 24.Bowe, B., Xie, Y., Xian, H., Balasubramanian, S., & Al-Aly, Z. (2016). Low levels of high-density lipoprotein cholesterol increase the risk of incident kidney disease and its progression. Kidney International, 89(4), 886–896. https://doi.org/10.1016/j.kint.2015.12.034 25.Breiman, L. (1984). Classification and Regression Trees. New York, 368. https://doi.org/10.1201/9781315139470 26.Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324 27.Breiman, L., Cutler, A., Liaw, A., & Wiener M. (2022). randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. R Package Version, 4.7-1.1. Available online: https://CRAN.R-project.org/package=randomForest (accessed on 03 March 2023). 28.Byeon, H. (2020). Is the Random Forest Algorithm Suitable for Predicting Parkinson's Disease with Mild Cognitive Impairment out of Parkinson's Disease with Normal Cognition?. International Journal of Environmental Research and Public Health, 17(7), 2594. https://doi.org/10.3390/ijerph17072594 29.Caesarendra, W., & Tjahjowidodo, T. (2017). A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines, 5(4), 21. https://doi.org/10.3390/machines5040021 30.Cao, X., Yang, B., & Zhou, J. (2022). Scoring model to predict risk of chronic kidney disease in Chinese health screening examinees with type 2 diabetes. International Urology and Nephrology, 54(7), 1629–1639. https://doi.org/10.1007/s11255-021-03045-9 31.Caravaca-Fontán, F., Azevedo, L., Bayo, M. Á., Gonzales-Candia, B., Luna, E., & Caravaca, F. (2017). High levels of both serum gamma-glutamyl transferase and alkaline phosphatase are independent preictors of mortality in patients with stage 4-5 chronic kidney disease. Niveles séricos elevados de gamma-glutamil transferasa y fosfatasa alcalina son predictores independientes de mortalidad en la enfermedad renal crónica estadio 4-5. Nefrologia, 37(3), 267–275. https://doi.org/10.1016/j.nefro.2016.11.010 32.Chang, H. J., Lin, K. R., Chang, J. L., & Lin, M. T. (2020). Risk Factors for Chronic Kidney Disease in Older Adults with Hyperlipidemia and/or Cardiovascular Diseases in Taipei City, Taiwan: A Community-Based Cross-Sectional Analysis. International Journal of Environmental Research and Public Health, 17(23), 8763. https://doi.org/10.3390/ijerph17238763 33.Chang, Y. P., Liao, C. M., Wang, L. H., Hu, H. H., & Lin, C. M. (2021). Static and Dynamic Prediction of Chronic Renal Disease Progression Using Longitudinal Clinical Data from Taiwan's National Prevention Programs. Journal of Clinical Medicine, 10(14), 3085. https://doi.org/10.3390/jcm10143085 34.Chen, T., & Guestrin, C. (2019, August). XGBoost: A Scalable Tree Boosting System. In the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17, pp. 785–794. 35.Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., and et al., (2022). Xgboost: Extreme Gradient Boosting. R Package Version, 1.6.0.1. Available online: https://CRAN.R-project.org/package=xgboost (accessed on 03 March 2023). 36.Cheng, C. H., Lin, C. Y., Cho, T. H., & Lin, C. M. (2021). Machine Learning to Predict the Progression of Bone Mass Loss Associated with Personal Characteristics and a Metabolic Syndrome Scoring Index. Healthcare, 9(8), 948. https://doi.org/10.3390/healthcare9080948 37.Cheng, F. Y., Joshi, H., Tandon, P., Freeman, R., Reich, D. L., Mazumdar, M., Kohli-Seth, R., Levin, M., Timsina, P., & Kia, A. (2020). Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients. Journal of Clinical Medicine, 9(6), 1668. https://doi.org/10.3390/jcm9061668 38.Chertow, G. M., Hsu, C. Y., & Johansen, K. L. (2006). The enlarging body of evidence: obesity and chronic kidney disease. Journal of the American Society of Nephrology, 17(6), 1501–1502. https://doi.org/10.1681/ASN.2006040327 39.Chiu, C. C., Wu, C. M., Chien, T. N., Kao, L. J., & Qiu, J. T. (2022). Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques. Healthcare, 10(6), 1087. https://doi.org/10.3390/healthcare10061087 40.Chiu, T. H., Huang, Y. C., Chiu, H., Wu, P. Y., Chiou, H. C., Huang, J. C., & Chen, S. C. (2020). Comparison of Various Obesity-Related Indices for Identification of Metabolic Syndrome: A Population-Based Study from Taiwan Biobank. Diagnostics, 10(12), 1081. https://doi.org/10.3390/diagnostics10121081 41.Chiu, Y. L., Jhou, M. J., Lee, T. S., Lu, C. J., & Chen, M. S. (2021). Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease. Risk Management and Healthcare Policy, 14, 4401–4412. https://doi.org/10.2147/RMHP.S319405 42.Choe, W. S., Choi, E. K., Han, K. D., Lee, E. J., Lee, S. R., Cha, M. J., & Oh, S. (2019). Association of metabolic syndrome and chronic kidney disease with atrial fibrillation: A nationwide population-based study in Korea. Diabetes Research and Clinical Practice, 148, 14–22. https://doi.org/10.1016/j.diabres.2018.12.004 43.Choi, D. K. (2019). Data-driven materials modeling with XGBoost algorithm and statistical inference analysis for prediction of fatigue strength of steels. International Journal of Precision Engineering and Manufacturing, 20(7), 129–138. https://doi.org/10.1007/s12541-019-00048-6 44.Choi, J. W., Choi, J. W., Kim, J. H., Yoo, K. B., & Park, E. C. (2015). Association between chronic disease and catastrophic health expenditure in Korea. BMC Health Services Research, 15, 26. https://doi.org/10.1186/s12913-014-0675-1 45.Cholaquidis, A., Fraiman, R., & Sued, M. (2020). On semi-supervised learning. TEST, 29, 914–937. https://doi.org/10.1007/s11749-019-00690-2 46.Chon, Y. E., Kim, H. J., Choi, Y. B., Hwang, S. G., Rim, K. S., Kim, M. N., Lee, J. H., Ha, Y., Lee, M. J. (2020). Decrease in waist-to-hip ratio reduced the development of chronic kidney disease in non-obese non-alcoholic fatty liver disease. Scientific Reports, 10(1), 8996. https://doi.org/10.1038/s41598-020-65940-y 47.Chou, Y. C., Kuan, J. C., Yang, T., Chou, W. Y., Hsieh, P. C., Bai, C. H., You, S. L., Chen, C. H., Wei, C. Y., & Sun, C. A. (2015). Elevated uric acid level as a significant predictor of chronic kidney disease: a cohort study with repeated measurements. Journal of Nephrology, 28(4), 457–462. https://doi.org/10.1007/s40620-014-0158-9 48.Coresh, J. (2017). Update on the Burden of CKD. Journal of the American Society of Nephrology, 28(4), 1020–1022. https://doi.org/10.1681/ASN.2016121374 49.Corsonello, A., Roller-Wirnsberger, R., Wirnsberger, G., Ärnlöv, J., Carlsson, A. C., Tap, L., Mattace-Raso, F., Formiga, F., Moreno-Gonzalez, R., Weingart, C., Sieber, C., Kostka, T., Guligowska, A., Gil, P., Lainez Martinez, S., Artzi-Medvedik, R., Melzer, I., & Lattanzio, F. (2020). Clinical Implications of Estimating Glomerular Filtration Rate with Three Different Equations Among Older People. Preliminary Results of the Project "Screening for Chronic Kidney Disease among Older People across Europe (SCOPE)". Journal of Clinical Medicine, 9(2), 294. https://doi.org/10.3390/jcm9020294 50.Cravedi, P., & Remuzzi, G. (2013). Pathophysiology of proteinuria and its value as an outcome measure in chronic kidney disease. British Journal of Clinical Pharmacology, 76(4), 516–523. https://doi.org/10.1111/bcp.12104 51.Cui, C., Zhang, W., Hong, Z., & Meng, L. (2020). Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering. International Journal of Digital Earth, 13(4), 1–17. https://doi.org/10.1080/17538947.2020.1808718 52.Curran, S. P., Famure, O., Li, Y., & Kim, S. J. (2014). Increased recipient body mass index is associated with acute rejection and other adverse outcomes after kidney transplantation. Transplantation, 97(1), 64–70. https://doi.org/10.1097/TP.0b013e3182a688a4 53.Dai, D., Chang, Y., Chen, Y., Chen, S., Yu, S., Guo, X., & Sun, Y. (2016). Visceral Adiposity Index and Lipid Accumulation Product Index: Two Alternate Body Indices to Identify Chronic Kidney Disease among the Rural Population in Northeast China. International Journal of Environmental Research and Public Health, 13(12), 1231. https://doi.org/10.3390/ijerph13121231 54.Damman, K., Valente, M. A., Voors, A. A., O'Connor, C. M., van Veldhuisen, D. J., & Hillege, H. L. (2014). Renal impairment, worsening renal function, and outcome in patients with heart failure: an updated meta-analysis. European Heart Journal, 35(7), 455–469. https://doi.org/10.1093/eurheartj/eht386 55.Dao, H. H. H., Nguyen, A. T., Vu, H. T. T., & Nguyen, T. N. (2022). Examine the Association between Metabolic Syndrome and Frailty in an Older Asian Population. Diabetology, 3(1), 108–116. https://doi.org/10.3390/diabetology3010009 56.Dash, S., Shakyawar, S.K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 54. https://doi.org/10.1186/s40537-019-0217-0 57.DeBoer, M. D., Filipp, S. L., Musani, S. K., Sims, M., Okusa, M. D., & Gurka, M. J. (2018). Metabolic Syndrome Severity and Risk of CKD and Worsened GFR: The Jackson Heart Study. Kidney and Blood Pressure Research, 43, 555–567. https://doi.org/10.1159/000488829. 58.Deja, A., Skrzypczyk, P., Leszczyńska, B., & Pańczyk-Tomaszewska, M. (2022). Reduced Blood Pressure Dipping Is A Risk Factor for the Progression of Chronic Kidney Disease in Children. Biomedicines, 10(9), 2171. https://doi.org/10.3390/biomedicines10092171 59.Department of Veterans Affairs (2020). Chronic Kidney Disease Prevention, Early Recognition, and Management, Veterans Health Administration Transmittal Sheet. Available online: https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=8737 (accessed on 03 March 2023). 60.Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv, arXiv, 1810.11363. https://doi.org/10.48550/arXiv.1810.11363. 61.Dossetor J. B. (1966). Creatininemia versus uremia. The relative significance of blood urea nitrogen and serum creatinine concentrations in azotemia. Annals of Internal Medicine, 65(6), 1287–1299. https://doi.org/10.7326/0003-4819-65-6-1287 62.Dunbray, N., Rane, R., Nimje, S., Katade, J., & Mavale, S. (2021). A Novel Prediction Model for Diabetes Detection Using Gridsearch and A Voting Classifier between Lightgbm and KNN. In the Proceedings of the 2021 2nd Global Conference for Advancement in Technology (GCAT), Bangalore, India, pp. 1–7. https://doi.org/10.1109/GCAT52182.2021.9587551. 63.Ebiaredoh-Mienye, S. A., Swart, T. G., Esenogho, E., & Mienye, I. D. (2022). A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease. Bioengineering, 9(8), 350. https://doi.org/10.3390/bioengineering9080350 64.Eibe, F., Hall, M., & Witten, I. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA, USA: Morgan Kaufmann Press. 65.Einhorn, D., Reaven, G. M., Cobin, R. H., Ford, E., Ganda, O. P., Handelsman, Y., Hellman, R., Jellinger, P. S., Kendall, D., Krauss, R. M., Neufeld, N. D., Petak, S. M., Rodbard, H. W., Seibel, J. A., Smith, D. A., & Wilson, P. W. (2003). American College of Endocrinology position statement on the insulin resistance syndrome. Endocrine Practice, 9(3), 237–252. 66.Elsayed, E. F., Sarnak, M. J., Tighiouart, H., Griffith, J. L., Kurth, T., Salem, D. N., Levey, A. S., & Weiner, D. E. (2008). Waist-to-hip ratio, body mass index, and subsequent kidney disease and death. American Journal of Kidney Diseases, 52(1), 29–38. https://doi.org/10.1053/j.ajkd.2008.02.363 67.Emerson, P. (2013). The original Borda count and partial voting. The Society for Social Choice and Welfare, 40(2), 353–358. https://doi.org/10.1007/s00355-011-0603-9 68.Evsevieva, M., Sergeeva, O., Mazurakova, A., Koklesova, L., Prokhorenko-Kolomoytseva, I., Shchetinin, E., Birkenbihl, C., Costigliola, V., Kubatka, P., & Golubnitschaja, O. (2022). Pre-pregnancy check-up of maternal vascular status and associated phenotype is crucial for the health of mother and offspring. EPMA Journal, 13(3), 351–366. https://doi.org/10.1007/s13167-022-00294-1 69.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. (2001). Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA, 285(19), 2486–2497. https://doi.org/10.1001/jama.285.19.2486 70.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010 71.Ford, E. S., Li, C., & Sattar, N. (2008). Metabolic syndrome and incident diabetes: current state of the evidence. Diabetes Care, 31(9), 1898–1904. https://doi.org/10.2337/dc08-0423 72.Forsyth, A. W., Barzilay, R., Hughes, K. S., Lui, D., Lorenz, K. A., Enzinger, A., Tulsky, J. A., & Lindvall, C. (2018). Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records. Journal of Pain and Symptom Management, 55(6), 1492–1499. https://doi.org/10.1016/j.jpainsymman.2018.02.016 73.Friedman, J. H. (1991) Multivariate Adaptive Regression Splines. Annals of Statistics, 19, 1–67. https://doi.org/10.1214/aos/1176347963 74.Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., Qian, J., & Yang, J. (2023). Glmnet: glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. 2023. R Package Version, 4.1-7. Available online: https://CRAN.R-project.org/package=glmnet (accessed on 03 March 2023). 75.Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems Man and Cybernetics Part C, 42(4), 463–484. https://doi.org/10.1109/TSMCC.2011.2161285 76.Gansevoort, R. T., Matsushita, K., van der Velde, M., Astor, B. C., Woodward, M., Levey, A. S., de Jong, P. E., Coresh, J., & Chronic Kidney Disease Prognosis Consortium. (2011). Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney International, 80(1), 93–104. https://doi.org/10.1038/ki.2010.531 77.Ge, S., Xu, X., Zhang, J., Hou, H., Wang, H., Liu, D., Zhang, X., Song, M., Li, D., Zhou, Y., Wang, Y., & Wang, W. (2019). Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study. EPMA Journal, 10(1), 65–72. https://doi.org/10.1007/s13167-019-0159-9 78.Gelber, R. P., Kurth, T., Kausz, A. T., Manson, J. E., Buring, J. E., Levey, A. S., & Gaziano, J. M. (2005). Association between body mass index and CKD in apparently healthy men. American Journal of Kidney Diseases, 46(5), 871–880. https://doi.org/10.1053/j.ajkd.2005.08.015 79.Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, Thrombosis, and Vascular Biology, 28(4), 629–636. https://doi.org/10.1161/ATVBAHA.107.151092 80.Guoping, Z. (2022). A graphic and tabular variable deduction method in logistic regression. Communications in Statistics - Theory and Methods, 51(16), 5412–5427. https://doi.org/10.1080/03610926.2020.1839499 81.Gupta, J., Mitra, N., Kanetsky, P. A., Devaney, J., Wing, M. R., Reilly, M., Shah, V. O., Balakrishnan, V. S., Guzman, N. J., Girndt, M., Periera, B. G., Feldman, H. I., Kusek, J. W., Joffe, M. M., Raj, D. S., & CRIC Study Investigators (2012). Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clinical Journal of the American Society of Nephrology, 7(12), 1938–1946. https://doi.org/10.2215/CJN.03500412 82.Gurka, M. J., Ice, C. L., Sun, S. S., & Deboer, M. D. (2012). A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences. Cardiovascular Diabetology, 11, 128. https://doi.org/10.1186/1475-2840-11-128 83.Gutiérrez-Esparza, G. O., Infante Vázquez, O., Vallejo, M., & Hernández-Torruco, J. (2020). Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms. Symmetry, 12(4), 581. https://doi.org/10.3390/sym12040581 84.Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182. 85.Hajhosseini, B., Longaker, M. T., & Gurtner, G. C. (2020). Pressure Injury. Annals of Surgery, 271(4), 671–679. https://doi.org/10.1097/SLA.0000000000003567 86.Han, D., Kolli, K. K., Gransar, H., Lee, J. H., Choi, S. Y., Chun, E. J., Han, H. W., Park, S. H., Sung, J., Jung, H. O., Min, J. K., & Chang, H. J. (2020). Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches. Journal of Cardiovascular Computed Tomography, 14(2), 168–176. https://doi.org/10.1016/j.jcct.2019.09.005 87.Han, W. Q., Xu, L., Tang, X. F., Chen, W. D., Wu, Y. J., & Gao, P. J. (2018). Membrane rafts-redox signalling pathway contributes to renal fibrosis via modulation of the renal tubular epithelial-mesenchymal transition. The Journal of Physiology, 596(16), 3603–3616. https://doi.org/10.1113/JP275952 88.Health Promotion Administration. (2007). Ministry of Health and Welfare. Metabolic Syndrome Criteria. Available online: https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=639&pid=1219 (accessed on 03 March 2023). 89.Hill, N. R., Fatoba, S. T., Oke, J. L., Hirst, J. A., O'Callaghan, C. A., Lasserson, D. S., & Hobbs, F. D. (2016). Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis. PLOS ONE, 11(7), e0158765. https://doi.org/10.1371/journal.pone.0158765 90.Hoffman, C., Rice, D., & Sung, H. Y. (1996). Persons with chronic conditions. Their prevalence and costs. JAMA, 276(18), 1473–1479. https://doi.org/10.1001/jama.1996.03540180029029 91.Hoi, S., Takata, T., Sugihara, T., Ida, A., Ogawa, M., Mae, Y., Fukuda, S., Munemura, C., & Isomoto, H. (2018). Predictive Value of Cortical Thickness Measured by Ultrasonography for Renal Impairment: A Longitudinal Study in Chronic Kidney Disease. Journal of Clinical Medicine, 7(12), 527. https://doi.org/10.3390/jcm7120527 92.Ikizler, T. A., Burrowes, J. D., Byham-Gray, L. D., Campbell, K. L., Carrero, J. J., Chan, W., Fouque, D., Friedman, A. N., Ghaddar, S., Goldstein-Fuchs, D. J., Kaysen, G. A., Kopple, J. D., Teta, D., Yee-Moon Wang, A., & Cuppari, L. (2020). KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. American Journal of Kidney Diseases, 76(3), S1–S107. https://doi.org/10.1053/j.ajkd.2020.05.006 93.Imran Ali, S., Ali, B., Hussain, J., Hussain, M., Satti, F. A., Park, G. H., & Lee, S. (2020). Cost-Sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis. Applied Sciences, 10(16), 5663. https://doi.org/10.3390/app10165663 94.Inker, L. A., Grams, M. E., Levey, A. S., Coresh, J., Cirillo, M., Collins, J. F., Gansevoort, R. T., Gutierrez, O. M., Hamano, T., Heine, G. H., Ishikawa, S., Jee, S. H., Kronenberg, F., Landray, M. J., Miura, K., Nadkarni, G. N., Peralta, C. A., Rothenbacher, D., Schaeffner, E., Sedaghat, S., … CKD Prognosis Consortium (2019). Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities: An Individual Participant Data Meta-analysis in a Global Consortium. American Journal of Kidney Diseases, 73(2), 206–217. https://doi.org/10.1053/j.ajkd.2018.08.013 95.International Diabetes Federation. (2006). The IDF consensus worldwide definition of the metabolic syndrome, Available online: https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome.html (accessed on 03 March 2023). 96.Ishigami, T., Yamamoto, R., Nagasawa, Y., Isaka, Y., Rakugi, H., Iseki, K., Yamagata, K., Tsuruya, K., Yoshida, H., Fujimoto, S., Asahi, K., Kurahashi, I., Ohashi, Y., Moriyama, T., & Watanabe, T. (2014). An association between serum γ-glutamyltransferase and proteinuria in drinkers and non-drinkers: a Japanese nationwide cross-sectional survey. Clinical and Experimental Nephrology, 18(6), 899–910. https://doi.org/10.1007/s10157-014-0938-5 97.Ismail W. N. (2023). Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease. Diagnostics, 13(15), 2501. https://doi.org/10.3390/diagnostics13152501 98.Jafar, T. H., Stark, P. C., Schmid, C. H., Landa, M., Maschio, G., de Jong, P. E., de Zeeuw, D., Shahinfar, S., Toto, R., Levey, A. S., & AIPRD Study Group (2003). Progression of chronic kidney disease: the role of blood pressure control, proteinuria, and angiotensin-converting enzyme inhibition: a patient-level meta-analysis. Annals of Internal Medicine, 139(4), 244–252. https://doi.org/10.7326/0003-4819-139-4-200308190-00006 99.Jafari, S., Shahbazi, Z., Byun, Y. C., & Lee, S. J. (2022). Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach. Mathematics, 10(6), 888. https://doi.org/10.3390/math10060888 100.Jager, K. J., Kovesdy, C., Langham, R., Rosenberg, M., Jha, V., & Zoccali, C. (2019). A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Nephrology Dialysis Transplantation, 34(11), 1803–1805. https://doi.org/10.1093/ndt/gfz174 101.Jeong, B., Cho, H., Kim, J., Kwon, S. K., Hong, S., Lee, C., Kim, T., Park, M. S., Hong, S., & Heo, T. Y. (2020). Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data. Diagnostics, 10(6), 415. https://doi.org/10.3390/diagnostics10060415 102.Jhee, J. H., Hwang, S. D., Song, J. H., & Lee, S. W. (2018). Upper Normal Serum Creatinine Concentrations as a Predictor for Chronic Kidney Disease: Analysis of 14 Years' Korean Genome and Epidemiology Study (KoGES). Journal of Clinical Medicine, 7(11), 463. https://doi.org/10.3390/jcm7110463 103.Jhou, M. J., Chen, M. S., Lee, T. S., Yang, C. T., Chiu, Y. L., & Lu, C. J. (2022). A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques. Healthcare, 10(12), 2496. https://doi.org/10.3390/healthcare10122496 104.Ji, A., Pan, C., Wang, H., Jin, Z., Lee, J. H., Wu, Q., Jiang, Q., & Cui, L. (2019). Prevalence and Associated Risk Factors of Chronic Kidney Disease in an Elderly Population from Eastern China. International Journal of Environmental Research and Public Health, 16(22), 4383. https://doi.org/10.3390/ijerph16224383 105.Johnson, D. W., Atai, E., Chan, M., Phoon, R. K., Scott, C., Toussaint, N. D., Turner, G. L., Usherwood, T., Wiggins, K. J., & KHA-CARI (2013). KHA-CARI guideline: Early chronic kidney disease: detection, prevention and management. Nephrology, 18(5), 340–350. https://doi.org/10.1111/nep.12052 106.Jordan, R. E., Lancashire, R. J., & Adab, P. (2011). An evaluation of Birmingham Own Health telephone care management service among patients with poorly controlled diabetes. A retrospective comparison with the General Practice Research Database. BMC Public Health, 11, 707. https://doi.org/10.1186/1471-2458-11-707 107.Kao, H. Y., Chang, C. C., Chang, C. F., Chen, Y. C., Cheewakriangkrai, C., & Tu, Y. L. (2022). Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease. International Journal of Environmental Research and Public Health, 19(3), 1219. https://doi.org/10.3390/ijerph19031219 108.Karimi-Alavijeh, F., Jalili, S., & Sadeghi, M. (2016). Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atherosclerosis, 12(3), 146–152. 109.Kassi, E., Pervanidou, P., Kaltsas, G., & Chrousos, G. (2011). Metabolic syndrome: definitions and controversies. BMC Medicine, 9, 48. https://doi.org/10.1186/1741-7015-9-48 110.Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal, 15, 104–116. https://doi.org/10.1016/j.csbj.2016.12.005 111.Ke, G., Meng, Q., Finley, T. W., Wang, T., Chen, W., Ma, W., Qiwei, Y., & Liu, T. (2017, December). LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, pp. 3147–3155. 112.Kelly, J., Gooding, P., Pratt, D., Ainsworth, J., Welford, M., & Tarrier, N. (2012). Intelligent real-time therapy: harnessing the power of machine learning to optimise the delivery of momentary cognitive-behavioural interventions. Journal of Mental Health, 21(4), 404–414. https://doi.org/10.3109/09638237.2011.638001 113.Kidney Disease Improving Global Outcomes. (2013). KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International, 3(1), 5–14. 114.Kim, H., Cho, H., & Ryu, D. (2020). Corporate Default Predictions Using Machine Learning: Literature Review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325 115.Krishnamurthy, S., Ks, K., Dovgan, E., Luštrek, M., Gradišek Piletič, B., Srinivasan, K., Li, Y. J., Gradišek, A., & Syed-Abdul, S. (2021). Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan. Healthcare, 9(5), 546. https://doi.org/10.3390/healthcare9050546 116.Kuhn, M. (2022). Caret: Classification and Regression Training. R Package Version, 6.0-93. Available online: https://CRAN.R-project.org/package=caret (accessed on 03 March 2023). 117.Kuma, A., Mafune, K., Uchino, B., Ochiai, Y., Enta, K., & Kato, A. (2022). Development of chronic kidney disease influenced by serum urate and body mass index based on young-to-middle-aged Japanese men: a propensity score-matched cohort study. BMJ Open, 12(2), e049540. https://doi.org/10.1136/bmjopen-2021-049540 118.Kuma, A., Uchino, B., Ochiai, Y., Kawashima, M., Enta, K., Tamura, M., Otsuji, Y., & Kato, A. (2018). Impact of low-density lipoprotein cholesterol on decline in estimated glomerular filtration rate in apparently healthy young to middle-aged working men. Clinical and Experimental Nephrology, 22(1), 15–27. https://doi.org/10.1007/s10157-017-1407-8 119.Kumar, V. (2021). Evaluation of computationally intelligent techniques for breast cancer diagnosis. Neural Computing and Applications, 33(8), 3195–3208. https://doi.org/10.1007/s00521-020-05204-y 120.Kuwabara, M., Hisatome, I., Roncal-Jimenez, C. A., Niwa, K., Andres-Hernando, A., Jensen, T., Bjornstad, P., Milagres, T., Cicerchi, C., Song, Z., Garcia, G., Sánchez-Lozada, L. G., Ohno, M., Lanaspa, M. A., & Johnson, R. J. (2017). Increased Serum Sodium and Serum Osmolarity Are Independent Risk Factors for Developing Chronic Kidney Disease; 5 Year Cohort Study. PLOS ONE, 12(1), e0169137. https://doi.org/10.1371/journal.pone.0169137 121.Lee, C. L., Liu, W. J., & Tsai, S. F. (2022). Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach. Nutrients, 14(14), 2832. https://doi.org/10.3390/nu14142832 122.Lee, J., Oh, K. H., & Park, S. K. (2021). Dietary Micronutrients and Risk of Chronic Kidney Disease: A Cohort Study with 12 Year Follow-Up. Nutrients, 13(5), 1517. https://doi.org/10.3390/nu13051517 123.Lee, M. Y., Huang, J. C., Chen, S. C., Chiou, H. C., & Wu, P. Y. (2018). Association of HbA1C Variability and Renal Progression in Patients with Type 2 Diabetes with Chronic Kidney Disease Stages 3⁻4. International Journal of Molecular Sciences, 19(12), 4116. https://doi.org/10.3390/ijms19124116 124.Lee, W. H., Hsu, P. C., Chu, C. Y., Chen, S. C., Chen, Y. C., Lee, M. K., Lee, H. H., Lee, C. S., Yen, H. W., Lin, T. H., Voon, W. C., Lai, W. T., Sheu, S. H., & Su, H. M. (2020). Upstroke Time as a Novel Predictor of Mortality in Patients with Chronic Kidney Disease. Diagnostics, 10(6), 422. https://doi.org/10.3390/diagnostics10060422 125.Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Literature Review. Risks, 7(1), 29. https://doi.org/10.3390/risks7010029 126.Levey, A. S., Eckardt, K. U., Tsukamoto, Y., Levin, A., Coresh, J., Rossert, J., De Zeeuw, D., Hostetter, T. H., Lameire, N., & Eknoyan, G. (2005). Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International, 67(6), 2089–2100. https://doi.org/10.1111/j.1523-1755.2005.00365. 127.Levin, A., Hemmelgarn, B., Culleton, B., Tobe, S., McFarlane, P., Ruzicka, M., Burns, K., Manns, B., White, C., Madore, F., Moist, L., Klarenbach, S., Barrett, B., Foley, R., Jindal, K., Senior, P., Pannu, N., Shurraw, S., Akbari, A., Cohn, A., … Canadian Society of Nephrology (2008). Guidelines for the management of chronic kidney disease. Canadian Medical Association Journal, 179(11), 1154–1162. https://doi.org/10.1503/cmaj.080351 128.Levy, G. D., Rashid, N., Niu, F., & Cheetham, T. C. (2014). Effect of urate-lowering therapies on renal disease progression in patients with hyperuricemia. The Journal of Rheumatology, 41(5), 955–962. https://doi.org/10.3899/jrheum.131159 129.Li, D. H. W., Chen, W., Li, S., & Lou, S. (2019). Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) - A case study of Hong Kong. Energy, 186, 115857. https://doi.org/10.1016/j.energy.2019.115857 130.Li, H., Wang, Q., Ke, J., Lin, W., Luo, Y., Yao, J., Zhang, W., Zhang, L., Duan, S., Dong, Z., & Chen, X. (2022a). Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China. Nutrients, 14(7), 1334. https://doi.org/10.3390/nu14071334 131.Li, L., Astor, B. C., Lewis, J., Hu, B., Appel, L. J., Lipkowitz, M. S., Toto, R. D., Wang, X., Wright, J. T., Jr, & Greene, T. H. (2012). Longitudinal progression trajectory of GFR among patients with CKD. American Journal of Kidney Diseases, 59(4), 504–512. https://doi.org/10.1053/j.ajkd.2011.12.009 132.Li, P. F., Lin, Y. J., Liang, Y. J., & Chen, W. L. (2022b). The Association between Human Epididymis Secretory Protein 4 and Metabolic Syndrome. Journal of Clinical Medicine, 11(9), 2362. https://doi.org/10.3390/jcm11092362 133.Li, P. K., Garcia-Garcia, G., Lui, S. F., Andreoli, S., Fung, W. W., Hradsky, A., Kumaraswami, L., Liakopoulos, V., Rakhimova, Z., Saadi, G., Strani, L., Ulasi, I., & Kalantar-Zadeh, K. (2020). Kidney health for everyone everywhere - from prevention to detection and equitable access to care. Clinical Nephrology, 93(3), 111–122. https://doi.org/10.5414/CNWKDEditorial 134.Li, R., Li, W., Lun, Z., Zhang, H., Sun, Z., Kanu, J. S., Qiu, S., Cheng, Y., & Liu, Y. (2016). Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health, 16, 296. https://doi.org/10.1186/s12889-016-2870-y 135.Li, Y., Zhao, L., Chen, Y., Liu, A., Liu, X., Shao, X., Zhang, Y., Wang, H., Wang, X., Li, B., Deng, K., Liu, Q., Holthöfer, H., & Zou, H. (2013). Association between metabolic syndrome and chronic kidney disease in perimenopausal women. International Journal of Environmental Research and Public Health, 10(9), 3987–3997. https://doi.org/10.3390/ijerph10093987 136.Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674 137.Lin, C. M. (2020). An Application of Metabolic Syndrome Severity Scores in the Lifestyle Risk Assessment of Taiwanese Adults. International Journal of Environmental Research and Public Health, 17(10), 3348. https://doi.org/10.3390/ijerph17103348 138.Lin, W. C., & Chen, C. (2021). Novel World University Rankings Combining Academic, Environmental and Resource Indicators. Sustainability, 13(24), 13873. https://doi.org/10.3390/su132413873 139.Lin, Y. T., Chen, M., Ho, C. C., & Lee, T. S. (2020). Relationships among Leisure Physical Activity, Sedentary Lifestyle, Physical Fitness, and Happiness in Adults 65 Years or Older in Taiwan. International Journal of Environmental Research and Public Health, 17(14), 5235. https://doi.org/10.3390/ijerph17145235 140.Liu, P., Zhang, Z., & Li, Y. (2021). Relevance of the Pyroptosis-Related Inflammasome Pathway in the Pathogenesis of Diabetic Kidney Disease. Frontiers in Immunology, 12, 603416. https://doi.org/10.3389/fimmu.2021.603416 141.Liu, Y., Chen, P. C., Krause, J., & Peng, L. (2019). How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. JAMA, 322(18), 1806–1816. https://doi.org/10.1001/jama.2019.16489 142.López, N. C., García-Ordás, M. T., Vitelli-Storelli, F., Fernández-Navarro, P., Palazuelos, C., & Alaiz-Rodríguez, R. (2021). Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction. International Journal of Environmental Research and Public Health, 18(20), 10670. https://doi.org/10.3390/ijerph182010670 143.Lv, J. C., & Zhang, L. X. (2019). Prevalence and Disease Burden of Chronic Kidney Disease. Advances in Experimental Medicine and Biology, 1165, 3–15. https://doi.org/10.1007/978-981-13-8871-2_1 144.Lv, Y., Le, Q. T., Bui, H. B., Bui, X. N., Nguyen, H., Nguyen-Thoi, T., Dou, J., & Song, X. (2020). A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer. Applied Sciences, 10(2), 635. https://doi.org/10.3390/app10020635 145.Mansour, O., Paik, J. M., Wyss, R., Mastrorilli, J. M., Bessette, L. G., Lu, Z., Tsacogianis, T., & Lin, K. J. (2023). A Novel Chronic Kidney Disease Phenotyping Algorithm Using Combined Electronic Health Record and Claims Data. Clinical Epidemiology, 15, 299–307. https://doi.org/10.2147/CLEP.S397020 146.Megari, K. (2013). Quality of Life in Chronic Disease Patients. Health Psychology Research, 1(3), e27. https://doi.org/10.4081/hpr.2013.e27 147.Mennuni, S., Rubattu, S., Pierelli, G., Tocci, G., Fofi, C., & Volpe, M. (2014). Hypertension and kidneys: unraveling complex molecular mechanisms underlying hypertensive renal damage. Journal of Human Hypertension, 28(2), 74–79. https://doi.org/10.1038/jhh.2013.55 148.Microsoft. (2022). LightGBM: Light Gradient Boosting Machine. R Package Version, 3.3.2. Available online: https://github.com/microsoft/LightGBM (accessed on 03 March 2023). 149.Milborrow, S. (2021). Derived from Mda: MARS by T. Hastie and R. Tibshirani. Earth: Multivariate Adaptive Regression Splines. R Package Version, 5.3.1. Available online: http://CRAN.R-project.org/package=earth (accessed on 03 March 2023). 150.Mitch, W. E., & Wilcox, C. S. (1982). Disorders of body fluids, sodium and potassium in chronic renal failure. The American Journal of Medicine, 72(3), 536–550. https://doi.org/10.1016/0002-9343(82)90523-x 151.Moghimi, A., Yang, C., & Marchetto, P. M. (2018). Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging. IEEE Access, 6, 56870–56884. https://doi.org/10.1109/ACCESS.2018.2872801 152.Moore, L. W., & Kalantar-Zadeh, K. (2019). Implementing the “Advancing American Kidney Health Initiative” by Leveraging Nutritional and Dietary Management of Kidney Patients. Journal of Renal Nutrition, 29(5), 357–360. https://doi.org/10.1053/j.jrn.2019.07.004 153.Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. (2019). State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies, 12(7), 1301. https://doi.org/10.3390/en12071301 154.Moubayed, A., Aqeeli E., & Shami A. (2020). Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection. In the Proceedings of the 33rd IEEE Canadian Conference on Electrical and Computer Engineering (CCECE'20), pp. 1–6. https://doi.org/10.48550/arXiv.2006.09272 155.Murphy, D., McCulloch, C. E., Lin, F., Banerjee, T., Bragg-Gresham, J. L., Eberhardt, M. S., Morgenstern, H., Pavkov, M. E., Saran, R., Powe, N. R., Hsu, C. Y., & Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team (2016). Trends in Prevalence of Chronic Kidney Disease in the United States. Annals of Internal Medicine, 165(7), 473–481. https://doi.org/10.7326/M16-0273 156.National Collaborating Centre for Chronic Conditions. (2008). Chronic Kidney Disease: National Clinical Guideline for Early Identification and Management in Adults in Primary and Secondary Care. UK, London: Royal College of Physicians: London. 157.Negi, K., & Mirza, A. (2020). Nephroprotective and Therapeutic Potential of Traditional Medicinal Plants in Renal Diseases. Journal of Drug Research in Ayurvedic Sciences, 5(3), 177–185. https://doi.org/10.5005/jdras-10059-0079 158.Ninomiya, T., Kiyohara, Y., Kubo, M., Yonemoto, K., Tanizaki, Y., Doi, Y., Hirakata, H., & Iida, M. (2006). Metabolic syndrome and CKD in a general Japanese population: the Hisayama Study. American Journal of Kidney Diseases, 48(3), 383–391. https://doi.org/10.1053/j.ajkd.2006.06.003 159.Noborisaka, Y., Ishizaki, M., Yamazaki, M., Honda, R., & Yamada, Y. (2013). Elevated Serum Gamma-Glutamyltransferase (GGT) Activity and the Development of Chronic Kidney Disease (CKD) in Cigarette Smokers. Nephro-urology Monthly, 5(5), 967–973. https://doi.org/10.5812/numonthly.13652 160.Obermayr, R. P., Temml, C., Gutjahr, G., Knechtelsdorfer, M., Oberbauer, R., & Klauser-Braun, R. (2008). Elevated uric acid increases the risk for kidney disease. Journal of the American Society of Nephrology, 19(12), 2407–2413. https://doi.org/10.1681/ASN.2008010080 161.Obuchowski, N. A. (2003). Receiver operating characteristic curves and their use in radiology. Radiology, 229(1), 3–8. https://doi.org/10.1148/radiol.2291010898 162.O'Neill, S., & O'Driscoll, L. (2015). Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obesity Reviews, 16(1), 1–12. https://doi.org/10.1111/obr.12229 163.Owens, E., Tan, K. S., Ellis, R., Del Vecchio, S., Humphries, T., Lennan, E., Vesey, D., Healy, H., Hoy, W., & Gobe, G. (2020). Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease. Biomedicines, 8(12), 606. https://doi.org/10.3390/biomedicines8120606 164.Park, S., Hamm, S.-Y., Jeon, H.-T., & Kim, J. (2017). Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS. Sustainability, 9(7), 1157. https://doi.org/10.3390/su9071157 165.Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F. X., Birgand, G., & Holmes, A. H. (2020). Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection, 26(5), 584–595. https://doi.org/10.1016/j.cmi.2019.09.009 166.Peralta, C. A., Whooley, M. A., Ix, J. H., & Shlipak, M. G. (2006). Kidney function and systolic blood pressure new insights from cystatin C: data from the Heart and Soul Study. American Journal of Hypertension, 19(9), 939–946. https://doi.org/10.1016/j.amjhyper.2006.02.007 167.Perazella, M. A., & Khan, S. (2006). Increased mortality in chronic kidney disease: a call to action. The American Journal of the Medical Sciences, 331(3), 150–153. https://doi.org/10.1097/00000441-200603000-00007 168.Perkins, N. J., & Schisterman, E. F. (2006). The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. American Journal of Epidemiology, 163(7), 670–675. https://doi.org/10.1093/aje/kwj063 169.Pes, B. (2020). Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. Neural Computing and Applications, 32(10), 5951–5973. https://doi.org/10.1007/s00521-019-04082-3 170.Poonia, R. C., Gupta, M. K., Abunadi, I., Albraikan, A. A., Al-Wesabi, F. N., Hamza, M. A., & B, T. (2022). Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease. Healthcare, 10(2), 371. https://doi.org/10.3390/healthcare10020371 171.Prasad, G. V. (2014). Metabolic syndrome and chronic kidney disease: Current status and future directions. World Journal of Nephrology, 3(4), 210–219. https://doi.org/10.5527/wjn.v3.i4.210 172.Qian, Y., Zhou, W., Yan, J., Li, W., & Han, L. (2014). Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery. Remote Sensing, 7(1), 153–168. https://doi.org/10.3390/rs70100153 173.Qin, J., Chen, L., Liu, Y., Liu, C., Feng, C., & Chen, B. (2019). A Machine Learning Methodology for Diagnosing Chronic Kidney Disease. IEEE Access, 8, 20991–21002. https://doi.org/10.1109/ACCESS.2019.2963053 174.Raikou, V. D., & Gavriil, S. (2018). Metabolic Syndrome and Chronic Renal Disease. Diseases, 6(1), 12. https://doi.org/10.3390/diseases6010012 175.Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259 176.Ramaswamy, R., Wee, S. N., George, K., Ghosh, A., Sarkar, J., Burghaus, R., & Lippert, J. (2021). CKD subpopulations defined by risk-factors: A longitudinal analysis of electronic health records. CPT: pharmacometrics and systems pharmacology, 10(11), 1343–1356. https://doi.org/10.1002/psp4.12695 177.Ranasinghe, P., Mathangasinghe, Y., Jayawardena, R., Hills, A. P., & Misra, A. (2017). Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: a systematic review. BMC Public Health, 17(1), 101. https://doi.org/10.1186/s12889-017-4041-1 178.Ravera, M., Re, M., Deferrari, L., Vettoretti, S., & Deferrari, G. (2006). Importance of blood pressure control in chronic kidney disease. Journal of the American Society of Nephrology, 17(4 Suppl 2), S98–S103. https://doi.org/10.1681/ASN.2005121319 179.Razzaghi, T., Roderick, O., Safro, I., & Marko, N. (2016). Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values. PLOS ONE, 11(5), e0155119. https://doi.org/10.1371/journal.pone.0155119 180.Reddy, Y. C. A. P., Viswanath, P., & Eswara Reddy, B. (2018). Semi-supervised learning: A brief review. International Journal of Engineering and Technology, 7(1-8), 81–85. https://doi.org/10.14419/ijet.v7i1.8.9977 181.Ross, E. G., Shah, N. H., Dalman, R. L., Nead, K. T., Cooke, J. P., & Leeper, N. J. (2016). The use of machine learning for the identification of peripheral artery disease and future mortality risk. Journal of Vascular Surgery, 64(5), 1515–1522.e3. https://doi.org/10.1016/j.jvs.2016.04.026 182.Ryu, S., Chang, Y., Kim, D. I., Kim, W. S., & Suh, B. S. (2007). gamma-Glutamyltransferase as a predictor of chronic kidney disease in nonhypertensive and nondiabetic Korean men. Clinical Chemistry, 53(1), 71–77. https://doi.org/10.1373/clinchem.2006.078980 183.Saberi-Karimian, M., Khorasanchi, Z., Ghazizadeh, H., Tayefi, M., Saffar, S., Ferns, G. A., & Ghayour-Mobarhan, M. (2021). Potential value and impact of data mining and machine learning in clinical diagnostics. Critical Reviews in Clinical Laboratory Sciences, 58(4), 275–296. https://doi.org/10.1080/10408363.2020.1857681 184.Sahathevan, S., Khor, B. H., Ng, H. M., Gafor, A. H. A., Mat Daud, Z. A., Mafra, D., & Karupaiah, T. (2020). Understanding Development of Malnutrition in Hemodialysis Patients: A Narrative Review. Nutrients, 12(10), 3147. https://doi.org/10.3390/nu12103147 185.Sanmarchi, F., Fanconi, C., Golinelli, D., Gori, D., Hernandez-Boussard, T., & Capodici, A. (2023). Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. Journal of Nephrology, 36(4), 1101–1117. https://doi.org/10.1007/s40620-023-01573-4 186.Saran, R., Robinson, B., Abbott, K. C., Agodoa, L. Y. C., Bhave, N., Bragg-Gresham, J., Balkrishnan, R., Dietrich, X., Eckard, A., Eggers, P. W., Gaipov, A., Gillen, D., Gipson, D., Hailpern, S. M., Hall, Y. N., Han, Y., He, K., Herman, W., Heung, M., Hirth, R. A., … Shahinian, V. (2018). US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States. American Journal of Kidney Diseases, 71(3), A7. https://doi.org/10.1053/j.ajkd.2018.01.002 187.Saran, R., Robinson, B., Abbott, K. C., Bragg-Gresham, J., Chen, X., Gipson, D., Gu, H., Hirth, R. A., Hutton, D., Jin, Y., Kapke, A., Kurtz, V., Li, Y., McCullough, K., Modi, Z., Morgenstern, H., Mukhopadhyay, P., Pearson, J., Pisoni, R., Repeck, K., Schaubel, D. E., Shamraj, R., Steffick, D., Turf, M., Woodside, K. J., Xiang, J., Yin, M., Zhang, X., & Shahinian, A. (2019). US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States. American Journal of Kidney Diseases, 75(1), A6–A7. https://doi.org/10.1053/j.ajkd.2019.09.003 188.Savin, I. (2013). A Comparative Study of the Lasso-type and Heuristic Model Selection Methods. Jahrbücher für Nationalökonomie und Statistik, 233(4), 526–549. https://doi.org/10.2139/ssrn.2104428 189.Schena, F. P., Anelli, V. W., Abbrescia, D. I., & Di Noia, T. (2022). Prediction of chronic kidney disease and its progression by artificial intelligence algorithms. Journal of Nephrology, 35(8), 1953–1971. https://doi.org/10.1007/s40620-022-01302-3 190.Scottish Intercollegiate Guidelines Network. (2008). Diagnosis and Management of Chronic Kidney Disease: A National Clinical Guideline. UK, Edinburgh: Elliott House. 191.Seki, M., Nakayama, M., Sakoh, T., Yoshitomi, R., Fukui, A., Katafuchi, E., Tsuda, S., Nakano, T., Tsuruya, K., & Kitazono, T. (2019). Blood urea nitrogen is independently associated with renal outcomes in Japanese patients with stage 3-5 chronic kidney disease: a prospective observational study. BMC Nephrology, 20(1), 115. https://doi.org/10.1186/s12882-019-1306-1 192.Shen, Z. W., Xing, J., Wang, Q. L., Faheem, A., Ji, X., Li, J., Bian, W. W., Jiang, Z., Li, X. J., Xue, F. Z., & Liu, J. (2017). Association between serum γ-glutamyltransferase and chronic kidney disease in urban Han Chinese: a prospective cohort study. International Urology and Nephrology, 49(2), 303–312. https://doi.org/10.1007/s11255-016-1429-2 193.Shih, C. C., Lu, C. J., Chen, G. D., & Chang, C. C. (2020). Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals. International Journal of Environmental Research and Public Health, 17(14), 4973. https://doi.org/10.3390/ijerph17144973 194.Silva, A. P., Viegas, C. S. B., Mendes, F., Macedo, A., Guilherme, P., Tavares, N., Dias, C., Rato, F., Santos, N., Faísca, M., Almeida, E. d., Neves, P. L., & Simes, D. C. (2020). Gla-Rich Protein (GRP) as an Early and Novel Marker of Vascular Calcification and Kidney Dysfunction in Diabetic Patients with CKD: A Pilot Cross-Sectional Study. Journal of Clinical Medicine, 9(3), 635. https://doi.org/10.3390/jcm9030635 195.Singh, A. K., & Kari, J. A. (2013). Metabolic syndrome and chronic kidney disease. Current Opinion in Nephrology and Hypertension, 22(2), 198–203. https://doi.org/10.1097/MNH.0b013e32835dda78 196.Smajić, J., Hasić, S., & Rašić, S. (2018). High-density lipoprotein cholesterol, apolipoprotein E and atherogenic index of plasma are associated with risk of chronic kidney disease. Medicinski Glasnik, 15(2), 115–121. https://doi.org/10.17392/962-18 197.Stauffer, M. E., & Fan, T. (2014). Prevalence of anemia in chronic kidney disease in the United States. PLOS ONE, 9(1), e84943. https://doi.org/10.1371/journal.pone.0084943 198.Su, C. T., Chang, Y. P., Ku, Y. T., & Lin, C. M. (2022a). Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study. Diagnostics, 12(10), 2454. https://doi.org/10.3390/diagnostics12102454 199.Su, W. Y., Chen, I. H., Gau, Y. C., Wu, P. Y., Huang, J. C., Tsai, Y. C., Chen, S. C., Chang, J. M., Hwang, S. J., & Chen, H. C. (2022b). Metabolic Syndrome and Obesity-Related Indices Are Associated with Rapid Renal Function Decline in a Large Taiwanese Population Follow-Up Study. Biomedicines, 10(7), 1744. https://doi.org/10.3390/biomedicines10071744 200.Sun, J., Ren, J., Hu, X., Hou, Y., & Yang, Y. (2021). Therapeutic effects of Chinese herbal medicines and their extracts on diabetes. Biomedicine and Pharmacotherapy, 142, 111977. https://doi.org/10.1016/j.biopha.2021.111977 201.Taiwan Society of Nephrology. (2015). Taiwan Chronic Kidney Disease Clinical Guidelines. Available online: https://www.tsn.org.tw/UI/H/H00202.aspx (accessed on 03 March 2023). 202.Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135 203.Tangri, N., Inker, L. A., Hiebert, B., Wong, J., Naimark, D., Kent, D., & Levey, A. S. (2017). A Dynamic Predictive Model for Progression of CKD. American Journal of Kidney Diseases, 69(4), 514–520. https://doi.org/10.1053/j.ajkd.2016.07.030 204.Tanner, R. M., Brown, T. M., & Muntner, P. (2012). Epidemiology of obesity, the metabolic syndrome, and chronic kidney disease. Current Hypertension Reports, 14(2), 152–159. https://doi.org/10.1007/s11906-012-0254-y 205.Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17, 168–192. https://doi.org/10.1016/j.aci.2018.08.003 206.Thomas, G., Sehgal, A. R., Kashyap, S. R., Srinivas, T. R., Kirwan, J. P., & Navaneethan, S. D. (2011). Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clinical Journal of the American Society of Nephrology, 6(10), 2364–2373. https://doi.org/10.2215/CJN.02180311 207.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267–288. https://www.jstor.org/stable/2346178 208.Ting, S. M., Nair, H., Ching, I., Taheri, S., & Dasgupta, I. (2009). Overweight, obesity and chronic kidney disease. Nephron Clinical Practice, 112(3), c121–c127. https://doi.org/10.1159/000214206 209.Toft, B. S., Hörberg, U., & Rasmussen, B. (2022). The ups and downs of lifestyle modification: An existential journey among persons with severe obesity. Scandinavian Journal of Caring Sciences, 36(1), 265–274. https://doi.org/10.1111/scs.12985 210.Tozawa, M., Iseki, C., Tokashiki, K., Chinen, S., Kohagura, K., Kinjo, K., Takishita, S., & Iseki, K. (2007). Metabolic syndrome and risk of developing chronic kidney disease in Japanese adults. Hypertension Research, 30(10), 937–943. https://doi.org/10.1291/hypres.30.937 211.Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments. Future Generation Computing Systems, 104, 187–200. https://doi.org/10.1016/j.future.2019.10.043. 212.Uchida, S. (2011). Differential diagnosis of chronic kidney disease (CKD): By primary diseases. Japan Medical Association Journal, 54(1), 22–26. 213.Valentina Stefanou, Anastasia Kanellou, Dionisios Antonopoulos, Dimitris Timbis, Dimitra Margari, Panagiota Xenou, Maria Dekavala, Raikou Marianna, Myrto Trianti, Ioannis Tsaknis, & Vladimiros Lougovois. (2022). Pomegranate for Diabetes and its’ Complications Amelioration. International Journal of Pharmaceutical and Bio Medical Science, 2(08), 249–279. https://doi.org/10.47191/ijpbms/v2-i8-03 214.van Rooy, M. J., & Pretorius, E. (2015). Metabolic syndrome, platelet activation and the development of transient ischemic attack or thromboembolic stroke. Thrombosis Research, 135(3), 434–442. https://doi.org/10.1016/j.thromres.2014.12.030 215.Vassalotti, J. A., Centor, R., Turner, B. J., Greer, R. C., Choi, M., Sequist, T. D., & National Kidney Foundation Kidney Disease Outcomes Quality Initiative (2016). Practical Approach to Detection and Management of Chronic Kidney Disease for the Primary Care Clinician. The American Journal of Medicine, 129(2), 153–162.e7. https://doi.org/10.1016/j.amjmed.2015.08.025 216.Vaziri, N. D. (2016). HDL abnormalities in nephrotic syndrome and chronic kidney disease. Nature Reviews Nephrology, 12(1), 37–47. https://doi.org/10.1038/nrneph.2015.180 217.Wang, C., Song, J., Ma, Z., Yang, W., Li, C., Zhang, X., Hou, X., Sun, Y., Lin, P., Liang, K., Gong, L., Wang, M., Liu, F., Li, W., Yan, F., Yang, J., Wang, L., Tian, M., Liu, J., Zhao, R., … Chen, L. (2014a). Fluctuation between fasting and 2-H postload glucose state is associated with chronic kidney disease in previously diagnosed type 2 diabetes patients with HbA1c ≥ 7%. PLOS ONE, 9(7), e102941. https://doi.org/10.1371/journal.pone.0102941 218.Wang, J., Xu, J., Zhao, C., Peng, Y., & Wang, H. (2019a). An ensemble feature selection method for high-dimensional data based on sort aggregation. Systems Science and Control Engineering, 7, 32–39. https://doi.org/10.1080/21642583.2019.1620658 219.Wang, P. C., Wu, Y. F., Lin, M. S., Lin, C. L., Chang, M. L., Chang, S. T., Weng, T. C., & Chen, M. Y. (2022a). The Impact of Hepatitis C Virus, Metabolic Disturbance, and Unhealthy Behavior on Chronic Kidney Disease: A Secondary Cross-Sectional Analysis. International Journal of Environmental Research and Public Health, 19(6), 3558. https://doi.org/10.3390/ijerph19063558 220.Wang, W. L., Liang, S., Zhu, F. L., Liu, J. Q., Wang, S. Y., Chen, X. M., & Cai, G. Y. (2019b). The prevalence of depression and the association between depression and kidney function and health-related quality of life in elderly patients with chronic kidney disease: a multicenter cross-sectional study. Clinical Interventions in Aging, 14, 905–913. https://doi.org/10.2147/CIA.S203186 221.Wang, W., Russell, A., Yan, Y., & Global Health Epidemiology Reference Group (GHERG) (2014b). Traditional Chinese medicine and new concepts of predictive, preventive and personalized medicine in diagnosis and treatment of suboptimal health. EPMA Journal, 5(1), 4. https://doi.org/10.1186/1878-5085-5-4 222.Wang, X., Zheng, Y., Zhao, Z., & Wang, J. (2015). Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding. Sensors, 15(7), 16225–16247. https://doi.org/10.3390/s150716225 223.Wang, Y. C., Tsai, D. J., Yen, L. C., Yao, Y. H., Chiang, T. T., Chiu, C. H., Lin, T. Y., Yeh, K. M., & Chang, F. Y. (2022b). Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance. Journal of Clinical Medicine, 11(5), 1437. https://doi.org/10.3390/jcm11051437 224.Warady, B. A., Abraham, A. G., Schwartz, G. J., Wong, C. S., Muñoz, A., Betoko, A., Mitsnefes, M., Kaskel, F., Greenbaum, L. A., Mak, R. H., Flynn, J., Moxey-Mims, M. M., & Furth, S. (2015). Predictors of Rapid Progression of Glomerular and Nonglomerular Kidney Disease in Children and Adolescents: The Chronic Kidney Disease in Children (CKiD) Cohort. American Journal of Kidney Diseases, 65(6), 878–888. https://doi.org/10.1053/j.ajkd.2015.01.008 225.Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8, 279–292. https://doi.org/10.1007/BF00992698 226.World Health Organization. (2020a). Hypertension. Genva, Switzerland: WHO. 227.World Health Organization. (2020b). Noncommunicable Diseases. Genva, Switzerland: WHO. 228.Wu, N., Qin, Y., Chen, S., Yu, C., Xu, Y., Zhao, J., Yang, X., Li, N., & Pan, X. F. (2021). Association between metabolic syndrome and incident chronic kidney disease among Chinese: A nation-wide cohort study and updated meta-analysis. Diabetes/Metabolism Research and Reviews, 37(7), e3437. https://doi.org/10.1002/dmrr.3437 229.Xu, X., Hu, J., Song, N., Chen, R., Zhang, T., & Ding, X. (2017). Hyperuricemia increases the risk of acute kidney injury: a systematic review and meta-analysis. BMC Nephrology, 18(1), 27. https://doi.org/10.1186/s12882-016-0433-1 230.Xue, Y., Huang, Z., Liu, G., Feng, Y., Xu, M., Jiang, L., & Xu, J. (2020). Association analysis of Suboptimal health Status: a cross-sectional study in China. PeerJ, 8, e10508. https://doi.org/10.7717/peerj.10508 231.Yamagata, K., Makino, H., Iseki, K., Ito, S., Kimura, K., Kusano, E., Shibata, T., Tomita, K., Narita, I., Nishino, T., Fujigaki, Y., Mitarai, T., Watanabe, T., Wada, T., Nakamura, T., Matsuo, S., & Study Group for Frontier of Renal Outcome Modifications in Japan (FROM-J) (2016). Effect of Behavior Modification on Outcome in Early- to Moderate-Stage Chronic Kidney Disease: A Cluster-Randomized Trial. PLOS ONE, 11(3), e0151422. https://doi.org/10.1371/journal.pone.0151422 232.Yan, J., Xu, T., Yu, Y., & Xu, H. (2021). Rainfall Forecast Model Based on the TabNet Model. Water, 13(9), 1272. https://doi.org/10.3390/w13091272 233.Yan, R., Liu Y., & Gao, R. X. (2012). Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines. Mechanical Systems and Signal Processing, 29(5), 474–484. https://doi.org/10.1016/j.ymssp.2011.11.022 234.Yan, X., Li, X., Lu, Y., Ma, D., Mou, S., Cheng, Z., Ding, Y., Yan, B., Zhang, X., & Hu, G. (2022). Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data. Evidence-based Complementary and Alternative Medicine, 2022, 6561721. https://doi.org/10.1155/2022/6561721 235.Yandex Technologies. (2022). CatBoost: unbiased boosting with categorical features. R Package Version, 1.0.6. Available online: https://github.com/CatBoost/CatBoost/ (accessed on 03 March 2023). 236.Yanez-Borjas, J. J., Valtierra-Rodriguez, M., Camarena-Martinez, D., & Amezquita-Sanchez, J. P. (2020). Statistical time features for global corrosion assessment in a truss bridge from vibration signals. Measurement, 160, 107858. https://doi.org/10.1016/j.measurement.2020.107858 237.Yu, J., Zhangzhong, L., Lan, R., Zhang, X., Xu, L., & Li, J. (2023). Ensemble Learning Simulation Method for Hydraulic Characteristic Parameters of Emitters Driven by Limited Data. Agronomy, 13(4), 986. https://doi.org/10.3390/agronomy13040986 238.Zahran, A., & Shoker, A. (2008). About CKD stage-3 subdivision proposal. Nephrology Dialysis Transplantation, 23(5), 1765–1766. https://doi.org/10.1093/ndt/gfm840 239.Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere, 10(7), 373. http://doi.org/10.3390/atmos10070373 240.Zeng, J., Liu, M., Wu, L., Wang, J., Yang, S., Wang, Y., Yao, Y., Jiang, B., & He, Y. (2016). The Association of Hypertriglyceridemic Waist Phenotype with Chronic Kidney Disease and Its Sex Difference: A Cross-Sectional Study in an Urban Chinese Elderly Population. International Journal of Environmental Research and Public Health, 13(12), 1233. https://doi.org/10.3390/ijerph13121233 241.Zhang, L., & Wang, H. (2009). Chronic kidney disease epidemic: cost and health care implications in China. Seminars in Nephrology, 29(5), 483–486. https://doi.org/10.1016/j.semnephrol.2009.06.012 242.Zhang, Y., & Shao, J. (2015). A systemic review of suboptimal health. Global Journal of Public Health, 2(3), 20. https://doi.org/10.14725/gjph.v2n3a1313 243.Zhao, H., Xiong, W. H., Zhao, X., Wang, L. M., & Chen, J. X. (2012). Development and evaluation of a traditional Chinese medicine syndrome questionnaire for measuring sub-optimal health status in China. Journal of Traditional Chinese Medicine, 32(2), 129–136. https://doi.org/10.1016/s0254-6272(13)60001-1 244.Zhou, Q., Li, Y. Q., Zhu, S. S., Liu, X. Y., Shao, X. F., Li, B., Wang, X. H., Zhang, Y., Wang, H. L., Li, J. M., Deng, K. P., Liu, Q., & Zou, H. Q. (2016). Association of waist-to-hip ratio with chronic kidney disease in non-diabetic subjects. Journal of Southern Medical University, 36(9), 1221–1225.
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