References


1.     RomagnaniP, Agarwal R, Chan JCN, Levin A, Kalyesubula R, Karam S, Nangaku M,Rodríguez-Iturbe B, Anders HJ. Chronic kidney disease. Nat Rev Dis Primers2025; 11: 8 [RCA] [PMID: 39885176 DOI: 10.1038/s41572-024-00589-9]

2.     LiZ, He R, Wang Y, Qu Z, Liu J, Yu R, Yang S. Global trends of chronic kidneydisease from 1990 to 2021: a systematic analysis for the global burden ofdisease study 2021. BMC Nephrol 2025; 26: 385 [RCA] [PMID: 40660180 DOI:10.1186/s12882-025-04309-7]

3.     StaniferJW, Muiru A, Jafar TH, Patel UD. Chronic kidney disease in low- andmiddle-income countries. Nephrol Dial Transplant 2016; 31:868-874 [RCA] [PMID:27217391 DOI: 10.1093/ndt/gfv466]

4.     Echouffo-TcheuguiJB, Kengne AP. Risk models to predict chronic kidney disease and itsprogression: a systematic review. PLoS Med 2012;9: e1001344 [RCA] [PMID:23185136 DOI: 10.1371/journal.pmed.1001344]

5.     ChenH, Huang Y, Chen L. Ensemble machine learning for predicting renal functiondecline in chronic kidney disease: development and external validation. FrontMed (Lausanne) 2025; 12: 1598065 [RCA] [PMID: 41221528 DOI:10.3389/fmed.2025.1598065]

6.     YuanS, Guo L, Xu F. Artificial intelligence in nephrology: predicting CKDprogression and personalizing treatment. Int Urol Nephrol 2025 [RCA] [PMID:41205136 DOI: 10.1007/s11255-025-04878-4]

7.     Zorrilla-VacaA, Mena GE, Ripolles-Melchor J, et al. Risk factors for acute kidney injury inan enhanced recovery pathway for colorectal surgery. Surg Today.2021;51(4):537–544. doi: 10.1007/s00595-020-02107-2.

8.     Zhu Y, Xu P, Wang Q, et al. Diclofenac–acetaminophencombination induced acute kidney injury in postoperative pain relief. J PharmPharm Sci. 2018;21(1):19–26. doi: 10.18433/J3SH21.

9.     Zhang Y, Jiang L, Wang B, et al. Epidemiological characteristics ofand risk factors for patients with postoperative acute kidney injury: amulticenter prospective study in 30 Chinese intensive care units. Int UrolNephrol. 2018;50(7):1319–1328. Epub 2018 Feb 26. PMID: 29480442. doi: 10.1007/s11255-018-1828-7.

10.   ZabrockiL, Marquardt F, Albrecht K, et al. Acute kidney injury after abdominal aorticaneurysm repair: current epidemiology and potential prevention. Int UrolNephrol. 2018;50(2):331–337. doi: 10.1007/s11255-017-1767-8.

11.   Yu Y, Zhang C, Zhang F, et al. Development and validation of arisk nomogram for postoperative acute kidney injury in older patientsundergoing liver resection: a pilot study. BMC Anesthesiol. 2022;22(1):22. doi:10.1186/s12871-022-01566-z.

12.   Yu J, Seo YJ, Lee SK, et al. Postoperative acute kidney injuryand risk factors for major adverse kidney events within 30 days of burnsurgery. Burns. 2025;51(6):107533. doi:10.1016/j.burns.2025.107533.

13.   YingT, Chan S, Lane S, et al. Acute kidney injury post-major orthopaedic surgery: asingle-Centre case-control study. Nephrology (Carlton). 2018;23(2):126–132.doi: 10.1111/nep.12942.

14.   Yang X, Zhang T, Zhou H, et al. Acute kidney injury as anindependent predicting factor for stage 3 or higher chronic kidney diseaseafter nephrectomy. Urol Oncol. 2023;41(3):149.e1-149–e9. doi:10.1016/j.urolonc.2022.10.011.

15.   Dritsas,E.; Trigka, M. Machine Learning Techniques for Chronic Kidney Disease RiskPrediction. Big Data Cogn. Comput. 2022,6, 98.

16.   Gogoi,P.; Valan, J. Interpretable Machine Learning for Chronic Kidney DiseasePrediction: A SHAP and Genetic Algorithm-Based Approach. Biomed. Mater. Devices2024, 3, 1384–1402.

17.   Singamsetty,S.; Ghanta, S.; Biswas, S.; Pradhan, A. Enhancing machine learning-basedforecasting of chronic renal disease with explainable AI. PeerJ Comput. Sci.2024, 10, e2291.

18.   DamenJAAG, Hooft L, Schuit E. Prediction models for cardiovascular disease risk inthe general population: systematic review. Bmj clinical research2016;353:i2416.

19.   Collins GS, Mallett S, Omar O,et al. Developingrisk prediction models for type 2 diabetes: a systematic review of methodologyand reporting. BMC Med 2011;9:103.

20.   Liu X, Li N, Lv L, et al. Improving precision of glomerularfiltration rate estimating model by ensemble learning. J Transl Med2017;15:231.

21.   Li N, Huang H, Qian H-Z, et al. Improving accuracy of estimatingglomerular filtration rate using artificial neural network: model developmentand validation. J Transl Med 2020;18:120.

22.   AbanayagamC, Banu R, Lim C, Tham YC, Cheng CY, Tan G, Ekinci E, Sheng B, McKay G, ShawJE, Matsushita K, Tangri N, Choo J,Wong TY. Artificial intelligence in chronickidney disease management: a scoping review. Theranostics 2025; 15: 4566-4578[RCA] [PMID:40225559 DOI: 10.7150/thno.108552]

23.   SimeriA, Pezzi G, Arena R, Papalia G, Szili-Torok T, Greco R, Veltri P, Greco G,Pezzi V, Provenzano M, Zaza G. Artificial intelligence in chronic kidneydiseases: methodology and potential applications. Int Urol Nephrol 2025; 57:159-168 [RCA] [PMID: 39052168 DOI:10.1007/s11255-024-04165-8]

24.   KhalidF, Alsadoun L, Khilji F, Mushtaq M, Eze-Odurukwe A, Mushtaq MM, Ali H, FarmanRO, Ali SM, Fatima R, Bokhari SFH. Predicting the Progression of ChronicKidneyDisease: A Systematic Review of Artificial Intelligence and MachineLearning Approaches. Cureus 2024;16: e60145 [RCA] [PMID: 38864072 DOI:10.7759/cureus.60145]

25.   RezkNG, Alshathri S, Sayed A, Hemdan EE. Explainable AI for Chronic Kidney DiseasePrediction in Medical IoT: Integrating GANs and Few-Shot Learning.Bioengineering (Basel) 2025; 12: 356 [RCA] [PMID: 40281716 DOI:10.3390/bioengineering12040356]

26.   HaixianL, Shu P, Zhao L, Chunfeng L, Lun L. Machine learning approaches for EGFRmutation status prediction in NSCLC: an updated systematic review. Front Oncol2025; 15: 1576461 [RCA] [PMID: 40708937 DOI: 10.3389/fonc.2025.1576461]

27.   TahaK. Machine learning in biomedical and health big data: a comprehensive surveywith empirical and experimental insights. J Big Data 2025; 12: 61 [DOI:10.1186/s40537-025-01108-7]

28.   Beam,A. L. & Kohane, I. S. Big data and machine learning in health care. JAMA319, 1317 (2018).

29.   Bzdok,D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat.Methods 15, 233–234 (2018).

30.   Rajula,H. S. R., Verlato, G., Manchia, M., Antonucci, N. & Fanos, V. Comparison ofconventional statistical methods with machine learning in medicine: diagnosis,drug development, and treatment. Med. (Mex.) 56, 455 (2020).

31.   Bzdok,D. October. Classical statistics and statistical learning in imagingneuroscience. Front. Neurosci. ;11, 7856 (2017).5.

32.   Ushio,Y. et al. Machine learning for morbid glomerular hypertrophy. Sci. Rep. 12,2563 (2022).

33.   Fabris,A. et al. Proteomic-based research strategy identified laminin subunit alpha 2as a potential urinary-specific biomarker for the medullary sponge kidneydisease. Kidney Int. 91, 459–468 (2017).

34.   Zheng,J.; Li, X.; Zhu, J.; Guan, S.; Zhang, S.; Wang, W. Interpretable machinelearning for predicting chronic kidney disease progression risk. Digit. Health2024, 10, 1–16.

35.   Iftikhar,H.; Hashem, A.F.; Qureshi, M.; Rodrigues, P.C.; Ali, S.; Gonzales Medina, R.I.;López-Gonzales, J.L. An intelligent hybrid ensemble model for early detectionof breast cancer in multidisciplinary healthcare systems. Diagnostics 2026, 16,377.

36.   Goswami,G.; Prasad, B. Comparison Among Deep Learning Approaches and Biomarkers inEarly Detection of Parkinson’s Disease. J. Comput. Cogn. Eng. 2025, 4, 132–141.

37.   Hu,F.; Yang, H.; Ren, Z.; Wang, X.; Zhou, H.; Yang, S.; Wei, S.; Hu, J.; Zhao, S.;Hu, H.; et al. Evolution of China’s industry-university–research–hospitalcollaboration in AI medical devices and implications for medical institutionsand public health governance. Front. Public Health 2026, 14, 1852483.

38.   Nandhini,G.; Aravinth, J. Chronic kidney disease prediction using machine learningtechniques. In Proceedings of the International Conference on Recent Trends onElectronics, Information, Communication & Technology (RTEICT), Bangalore,India, 27–28 August 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 227–232.

39.   Halder,R.; Uddin, M.; Uddin, M.; Aryal, S.; Saha, S.; Hossen, R.; Ahmed, S.; Rony, M.;Akter, M. ML-CKDP: Machine learning-based chronic kidney disease predictionwith smart web application. J. Pathol. Inform. 2024, 15, 100371.

40.   Nandi,A.; Paul, P.; Das, A.; Ghosh, A.; Singh, A.; Gupta, A. Predictive Modelling forChronic Kidney Disease: A Hybrid Approach Using Exploratory Data Analysis,Machine Learning and Explainable AI. In Proceedings of the 8th InternationalConference on Electronics, Materials Engineering & Nano-Technology(IEMENTech), Kolkata, India, 31 January–2 February 2025; IEEE: Piscataway, NJ,USA, 2025; pp. 1–6.

41.   Gogoi,P.; Valan, J. Machine learning approaches for predicting and diagnosing chronickidney disease: Current trends, challenges, solutions, and future directions.Int. Urol. Nephrol. 2024, 57, 1245–1268.

 

 

 

 

 

42.   Rahman,M.; Al-Amin, M.; Hossain, J. Machine learning models for chronic kidney diseasediagnosis and prediction. Biomed.Signal Process. Control 2024, 87, 105368.

43.   Daniyal,M.; Qureshi, M.; Marzo, R.R.; Aljuaid, M.; Shahid, D. Exploring clinicalspecialists’ perspectives on the future role of AI: Evaluating replacementperceptions, benefits, and drawbacks. BMC Health Serv. Res. 2024, 24, 587.

44.   Hu,X.; Lu, Y.; Tian, G.; Bing, P.; Wang, B.; He, B. Predicting Herb-diseaseAssociations Through Graph Convolutional Network. Curr. Bioinform. 2023, 18,610–619.

45.   Wang,S.; Yang, C.; Chen, L. LSA-DDI: Learning Stereochemistry-Aware DrugInteractions via 3D Feature Fusion and Contrastive Cross-Attention. Int. J.Mol. Sci. 2025, 26, 6799.

46.   Dharmarathne,G.; Bogahawaththa, M.; Mcafee, M.; Rathnayake, U.; Meddage, D. On the diagnosisof chronic kidney disease using a machine learning-based interface withexplainable artificial intelligence. Intell. Syst. Appl. 2024, 22, 200397.

47.   Qin,J.; Chen, L.; Liu, Y.; Liu, C.; Feng, C.; Chen, B. A Machine LearningMethodology for Diagnosing Chronic Kidney Disease. IEEE Access 2023, 8,20991–21002.

48.   Zeuchner J, Elander L, Frisk J, et al. Incidence and trajectories ofsubclinical and KDIGO-defined postoperative acute kidney injury in patientsundergoing major abdominal surgery. BJA Open. 2024;12:100345. doi:10.1016/j.bjao.2024.100345.

49.   ZauritoP, Calado A, Quarta L, et al. Incidence and predictors of acute kidney injuryand acute kidney disease after robot-assisted radical prostatectomy in prostatecancer patients. Eur Urol Oncol. 2025;S2588-9311(25)00191-9. doi:10.1016/j.euo.2025.06.011.

50.   Xu L, Li C, Zhao L, et al. Acute kidney injury afternephrectomy: a new nomogram to predict postoperative renal function. BMCNephrol. 2020;21(1):181. doi: 10.1186/s12882-020-01839-0

51.   Xu L, Li C, Gao S, et al. Personalized prediction oflong-term renal function prognosis following nephrectomy using interpretablemachine learning algorithms: case-control study. JMIR Med Inform.2024;12:e52837. doi: 10.2196/52837.

52.   Zuo F, Zhong L, Min J, et al. Construction and validation of riskprediction models for renal replacement therapy in patients with acutepancreatitis. Eur J Med Res. 2025;30(1):70. doi: 10.1186/s40001-025-02345-5.

53.   Islam,M.; Majumder, M.; Hussein, M. Chronic kidney disease prediction based onmachine learning algorithms. J. Pathol. Inform. 2023, 14, 100189.

54.   Li,X.; Xiang, J.; Wu, F.; Li, M. A Dual Ranking Algorithm Based on the MultiplexNetwork for Heterogeneous Complex Disease Analysis. IEEE/ACM Trans. Comput.Biol. Bioinform. 2022, 19, 1993–2002.

55.   Hu,F.; Yang, H.; Zhou, X.; Zhao, S.; Qiu, L.; Wei, S.; Wang, X.; Hu, J.; Chen, Y.;Hu, H.; et al. Rehabilitation assistive technology transfer in the AI era:Network dynamics and public health impacts in the Yangtze River Delta, China.Front. Public Health 2026, 14, 1752396.

56.   GaoY, Li C, Chang W, Du B, Ye X, Yeo YH, Shi Y. Multi-modal AI for opportunisticscreening, staging and progression risk stratification of steatotic liverdisease. Nat Commun 2026;17:1562.

57.   Collins GS, Reitsma JB, Altman DG,et al. Transparentreporting of a multivariable prediction model for individual prognosis ordiagnosis (TRIPOD): the TRIPOD statement. Eur Urol 2015;67:1142–51.

58.   NielO, Bastard P. Artificial intelligence in nephrology: core concepts, clinicalapplications, and perspectives. American Journal of Kidney Diseases2019;74:803–10.

59.   HametP, Tremblay J. Artificial intelligence in medicine. Metabolism. clinical andexperimental. 2017:69s–S36.

60.   JohnsonKW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence incardiology. J Am Coll Cardiol 2018;71:2668–79.

61.   YuK-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng2018;2:719–31.

62.   WynantsL, Van Calster B, Collins GS, et al. Prediction models for diagnosis andprognosis of covid-19: systematic review and critical appraisal. BMJ2020;369:m1328.

63.   Ushio,Y. et al. Machine learning for morbid glomerular hypertrophy. Sci. Rep. 12,2563 (2022).

64.   GanesanS, Somasiri N. Navigating the integration of machine learning in healthcare:challenges, strategies, and ethical considerations. J Comput Cogn Eng2025;4:8-23.

65.   Chittora,P.; Chaurasia, S.; Chakrabarti, P.; Kumawat, G.; Chakrabarti, T.; Leonowicz,Z.; Jasinski, M.; Jasi´nski, Ł.; Goˇno, R.;Jasi´nska, E.; et al. Prediction ofChronic Kidney Disease—A Machine Learning Perspective. IEEE Access 2022, 9,17312–17334.

66.   Sanmarchi,F.; Fanconi, C.; Golinelli, D.; Gori, D.; Hernandez-Boussard, T.; Capodici, A.Predict, diagnose, and treat chronic kidney disease with machine learning: Asystematic literature review. J. Nephrol. 2023, 36, 1101–1117.

67.   DebalD, Sitote T. Chronic kidney disease prediction using machine learningtechniques. J Big Data 2022;9:109.

68.   IftikharH, Hashem AF, Qureshi M, Rodrigues PC. Clinical application of machine learningmodels for early-stage chronic kidney disease detection. Diagnostics2025;15:2610.

69.   IftikharH, Khan M, Khan Z, Khan F, Alshanbari HM, Ahmad Z. A comparative analysis ofmachine learning models: a case study in predicting chronic kidney disease.Sustainability 2023;15:2754.

70.   GeC, Shi Z, He J, Feng X, Shang K, Liao X, Liu Y, Jiang Y, Liu S. Cyanoglycosidesisolated from Moringa oleifera seeds inhibited PFKFB3/TGF-β1/Smads pathway toalleviate diabetic nephropathy through driving metabolic reprogramming. PharmBiol 2026;64:130-142.

71.   XiangJ, Zhang J, Zheng R, Li X, Li M. NIDM: network impulsive dynamics on multiplexbiological network for disease-gene prediction. Brief Bioinform2021;22:bbab080.

72.   KrishnamurthyS, Ks K, Dovgan E, Luštrek M, Gradišek Piletič B, Srinivasan K, Li YJ, GradišekA, Syed-Abdul S. Machine Learning Prediction Models for Chronic Kidney DiseaseUsing National Health Insurance Claim Data in Taiwan. Healthcare (Basel) 2021;9: 546 [RCA] [PMID: 34067129 DOI: 10.3390/healthcare9050546]

73.   KandaE, Suzuki A, Makino M, Tsubota H, Kanemata S, Shirakawa K, Yajima T. Machinelearning models for prediction of HF and CKD development in early-stage type 2diabetes patients. Sci Rep 2022; 12: 20012 [RCA] [PMID: 36411366 DOI:10.1038/s41598-022-24562-2]

74.   LuoH, Li J, Huang H, Jiao L, Zheng S, Ying Y, Li Q. AI-based segmentation of renalenhanced CT images for quantitative evaluate of chronic kidney disease. Sci Rep2024; 14: 16890 [RCA] [PMID: 39043766 DOI: 10.1038/s41598-024-67658-7]

75.   BrankovicA, Hassanzadeh H, Good N, Mann K, Khanna S, Abdel-Hafez A, Cook D. Explainablemachine learning for real-time deterioration alert prediction to guidepre-emptive treatment. Sci Rep 2022; 12: 11734 [RCA] [PMID: 35817885 DOI:10.1038/s41598-022-15877-1]

 

 

 

76.   BaronJM, Huang R, McEvoy D, Dighe AS. Use of machine learning to predict clinicaldecision support compliance, reduce alert burden, and evaluate duplicatelaboratory test ordering alerts. JAMIA Open 2021; 4: ooab006 [RCA] [PMID:33709062 DOI: 10.1093/jamiaopen/ooab006]

77.   ChiuLW, Ku YE, Chan FY, Lie WN, Chao HJ, Wang SY, Shen WC, Chen HY. Machinelearning algorithms to predict colistin-induced nephrotoxicity from electronichealth records in patients with multidrug-resistant Gram-negative infection.Int J Antimicrob Agents 2024; 64:107175 [RCA] [PMID: 38642812 DOI:10.1016/j.ijantimicag.2024.107175]

78.   HsiehCC, Hsieh CW, Uddin M, Hsu LP, Hu HH, Syed-Abdul S. Using machine learningmodels for predicting monthly iPTH levels in hemodialysis patients. ComputMethods Programs Biomed 2025; 260: 108541 [RCA] [PMID: 39637702 DOI:10.1016/j.cmpb.2024.108541]

79.   WuJ, Gao Q, Tian M, Tan S, Dong J, Wei H. Explainable machine learning predictionof 1-year kidney function progression among patients with type 2 diabetesmellitus and chronic kidney disease: a retrospective study. QJM 2025; 118:647-656 [RCA] [PMID: 40272892 DOI:10.1093/qjmed/hcaf101]

80.   AbbasQ, Jeong W, Lee SW. Explainable AI in Clinical Decision Support Systems: AMeta-Analysis of Methods, Applications, and Usability Challenges. Healthcare(Basel) 2025; 13: 2154 [RCA] [PMID: 40941506 DOI: 10.3390/healthcare13172154]

81.   Tun HM, Rahman HA, Naing L, Malik OA. Trust in ArtificialIntelligence-Based Clinical Decision Support Systems Among Health Care Workers:Systematic Review. J Med Internet Res 2025; 27: e69678 [RCA] [PMID: 40772775DOI: 10.2196/69678]

82.   MonlezunDJ, Omutoko L, Oduor P, Kokonya D, Rayel J, Sotomayor C, Girault MI, Los RíosUriarte ME, Sinyavskiy O, Aksamit T, Dugani SB, Garcia A, Gallagher C.Digitalization of health care in low- and middle-income countries. Bull WorldHealth Organ 2025; 103: 148-154 [RCA] [PMID: 39882493 DOI:10.2471/BLT.24.291643]

83.   LiuX, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics withmachine learning to uncover the intricacies of kidney disease. Brief Bioinform2024; 25: bbae364 [RCA][PMID:39082652DOI:10.1093/bib/bbae364]

84.   LinY, Shi T, Kong G. Acute kidney injury prognosis prediction using machinelearning methods:a systematic kidney review Med.2025;7(1):100936. doi:10.1016/j.xkme.2024.100936.

85.   TakkavatakarnK, Hofer IS. Artificial intelligence and machine learning in perioperativeacute kidney injury. Adv Kidney Dis Health. 2023;30(1):53–60. Epub 2022 Dec 8.doi: 10.1053/j.akdh.2022.10.001

86.   GBDChronic Kidney Disease Collaboration. Global, regional, and national burden ofchronic kidney disease, 1990-2017: a systematic analysis for the Global Burdenof Disease Study 2017. Lancet Lond Engl. 2020;395:709–733. doi:10.1016/S0140-6736(20)30045-3.

87.   GeorgeC, Echouffo-Tcheugui JB, Jaar BG, et al. The need for screening, earlydiagnosis, and prediction of chronic kidney disease in people with diabetes inlow- and middle-income countries-a review of the current literature. BMC Med.2022;20(1):247. doi: 10.1186/s12916-022-02438-6.

88.    Rajkomar A, Dean J, Kohane I.Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.doi: 10.1056/NEJMra1814259.

89.   Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality,risk of bias, and applicability assessment tool for prediction models usingregression or artificial intelligence methods. BMJ. 2025;388:e082505. doi:10.1136/bmj-2024-082505.

90.   Tangri N, Ferguson TW, Bamforth RJ, etal. Machine learningfor prediction of chronic kidney disease progression: validation of theKlinrisk model in the CANVAS Program and CREDENCE trial. Diabetes Obes Metab.2024;26(8):3371–3380. doi: 10.1111/dom.15678.

91.   Cai J, Luo J, Wang S, et al. Feature selection in machinelearning: a new perspective. Neurocomputing. 2018;300:70–79. doi:10.1016/j.neucom.2017.11.077.

92.   BinsonVA, Thomas S, Subramoniam M, et al. A review of machine learning algorithms forbiomedical applications. Ann Biomed Eng. 2024;52(5):1159–1183. doi:10.1007/s10439-024-03459-3.

93.   NielO, Boussard C, Bastard P. Artificial intelligence can predict GFR declineduring the course of ADPKD. Am J Kidney Dis 2018;71:911–2.

94.   Navarro CLA, Damen JAA, Takada T,et al. Risk ofbias in studies on prediction models developed using supervised machinelearning techniques: systematic review. BMJ. Oct 20, 2021;375:n2281. [doi:10.1136/bmj.n2281]

95.   ChristodoulouE, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematicreview shows no performance benefit of machine learning over logisticregression for clinical prediction models. J Clin Epidemiol. Jun2019;110:12-22. [doi: 10.1016/j.jclinepi.2019.02.004]

96.   Gallitto G, Englert R, Kincses B,et al. Externalvalidation of machine learning models-registered models and adaptive samplesplitting. GigaScience. Jan 6, 2025;14:giaf036. [doi:10.1093/gigascience/giaf036]

97.   Aoki J, Kaya C, Khalid O, et al. CKD progression prediction in adiverse US population: a machine-learning model. Kidney Med. Sep2023;5(9):100692. [doi: 10.1016/j.xkme.2023.100692]

98.   DelrueC, De Bruyne S, Speeckaert MM. Application of machine learning in chronickidney disease: current status and future prospects. Biomedicines. Mar 3,2024;12(3):568. [doi: 10.3390/biomedicines12030568]

99.   XieY, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, Maddukuri G, Tsai CY, Floyd T,Al-Aly Z. Analysis of the Global Burden of Disease study highlights the global,regional, and national trends of chronic kidney disease epidemiology from 1990to 2016. Kidney Int. 2018;94:567–81.

100. FrancisA, Harhay MN, Ong ACM, Tummalapalli SL, Ortiz A, Fogo AB, Fliser D,Roy-Chaudhury P, Fontana M, Nangaku M, et al. chronic kidney disease and theglobal public health agenda: an international consensus. Nat Rev Nephrol.2024;20(7):473–85.

101. NawazS, Chinnadurai R, Al-Chalabi S, Evans P, Kalra PA, Syed AA, Sinha S. Obesityand chronic kidney disease: a current review. Obes Sci Pract. 2023;9:61–74.

102. PommerW. Preventive nephrology: the role of obesity in different stages of chronickidney disease. Kidney Diseases. 2018;4:199–204.

103. El-SofanyH, Bouallegue B, El-Latif YMA. A proposed technique for predicting heartdisease using machine learning algorithms and an explainable AI method. SciRep. 2024;14:23277.

104. aito H, Yoshimura H, Tanaka K,et al. PredictingCKD progression using time-series clustering and light gradient boostingmachines. Sci Rep. Jan 19, 2024;14(1):1723. [doi: 10.1038/s41598-024-52251-9]

 

 

 

 

 

105. BaiQ, Su C, Tang W, Li Y. Machine learning to predict end stage kidney disease inchronic kidney disease. Sci Rep.May 19, 2022;12(1):8377. [doi:10.1038/s41598-022-12316-z]

106. Stevens, P. E. et al. KDIGO 2024 clinical practiceguideline for the evaluation and management of chronic kidney disease. KidneyInt. 105, S117–S314 (2024).

107. Aljaaf,A.J.; Al-Jumeily, D.; Haglan, H.M.; Alloghani, M.; Baker, T.; Hussain, A.J.;Mustafina, J. Early prediction of chronic kidney disease using machine learningsupported by predictive analytics. In 2018 IEEE Congress on EvolutionaryComputation (CEC); IEEE: 102.Piscataway, NJ, USA, 2018.Available online:https://ieeexplore.ieee.org/document/8477876

108. Lundberg,S.; Lee, S. A unified approach to interpreting model predictions. In Advancesin Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2017;Volume30.

109. Polat,K.; Güne¸s, S. A hybrid approach for chronic kidney disease classificationusing feature selection and machine learning methods. Appl. Soft Comput. 2017,61, 999–1006.

110. Arshad,I.; Umair, M.; Jan, F.; Iftikhar, H.; Rodrigues, P.C.; Armas, E.A.T.;López-Gonzales, J.L. Performance of classification algorithms under classimbalance: Simulation and real-world evidence. IEEE Access 2025, 13,179672–179685.