{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T08:46:51Z","timestamp":1785833211826,"version":"3.56.0"},"reference-count":77,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD) by examining the most recent literature. Articles published from 2016 to 2025 were collected from online databases such as PubMed, Web of Science, and Embase. After abstract and full-text screening, 57 articles were included in the results section. Machine learning was applied to clinical and laboratory data, medical imaging, urine samples, retinal images, and at-home measurements to diagnose CKD and predict CKD progression and related complications. Although many studies reported high discriminatory performance, the evidence base was dominated by retrospective, single-center, and methodologically heterogeneous studies, with frequent high-risk-of-bias findings and limited external validation. Furthermore, most published models are not yet sufficiently validated for clinical deployment. Before these tools can be adopted in routine care, prospective, multicenter studies are required that report calibration and clinical utility, adhere to established reporting standards, and demonstrate added value over the current standard of care.<\/jats:p>","DOI":"10.3390\/diagnostics16152354","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:54:26Z","timestamp":1785153266000},"page":"2354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4875-0170","authenticated-orcid":false,"given":"Leon","family":"Van de Putte","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9183-4390","authenticated-orcid":false,"given":"Marijn M.","family":"Speeckaert","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium"},{"name":"Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium"},{"name":"Research Foundation-Flanders (FWO), 1000 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/dom.15683","article-title":"What every clinician needs to know about chronic kidney disease: Detection, classification and epidemiology","volume":"26","author":"Selby","year":"2024","journal-title":"Diabetes Obes. Metab."},{"key":"ref_2","first-page":"554","article-title":"Chronic Kidney Disease: Prevention, Diagnosis, and Treatment","volume":"108","author":"Goodbred","year":"2023","journal-title":"Am. Fam. Physician"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"KDIGO (2024). Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int., 105, S117\u2013S314.","DOI":"10.1016\/j.kint.2023.10.018"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S0140-6736(20)30045-3","article-title":"Global, regional, and national burden of chronic kidney disease, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"395","author":"Bikbov","year":"2020","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2403645","DOI":"10.1080\/0886022X.2024.2403645","article-title":"Epidemiological analysis of chronic kidney disease from 1990 to 2019 and predictions to 2030 by Bayesian age-period-cohort analysis","volume":"46","author":"Dong","year":"2024","journal-title":"Ren. Fail."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.pop.2020.08.001","article-title":"Chronic Kidney Disease","volume":"47","author":"Charles","year":"2020","journal-title":"Prim. Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1001\/archinte.163.12.1417","article-title":"Progression of chronic renal failure","volume":"163","author":"Yu","year":"2003","journal-title":"Arch. Intern Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1042\/CS20120198","article-title":"Inflammation and the pathogenesis of diabetic nephropathy","volume":"124","author":"Wada","year":"2013","journal-title":"Clin. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fletcher, B.R., Damery, S., Aiyegbusi, O.L., Anderson, N., Calvert, M., Cockwell, P., Ferguson, J., Horton, M., Paap, M.C.S., and Sidey-Gibbons, C. (2022). Symptom burden and health-related quality of life in chronic kidney disease: A global systematic review and meta-analysis. PLoS Med., 19.","DOI":"10.1371\/journal.pmed.1003954"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/s41581-021-00518-z","article-title":"Patient-centred approaches for the management of unpleasant symptoms in kidney disease","volume":"18","author":"Lockwood","year":"2022","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1016\/S0140-6736(16)32064-5","article-title":"Chronic Kidney Disease","volume":"389","author":"Webster","year":"2017","journal-title":"Lancet"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s40620-018-0531-1","article-title":"Ultrasound and color Doppler applications in chronic kidney disease","volume":"31","author":"Petrucci","year":"2018","journal-title":"J. Nephrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/S0140-6736(21)00519-5","article-title":"Chronic kidney disease","volume":"398","author":"Jafar","year":"2021","journal-title":"Lancet"},{"key":"ref_14","unstructured":"Choi, R.Y., Coyner, A.S., Kalpathy-Cramer, J., Chiang, M.F., and Campbell, J.P. (2020). Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl. Vis. Sci. Technol., 9."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ashenden, S.K., Bartosik, A., Agapow, P.-M., and Semenova, E. (2021). Introduction to artificial intelligence and machine learning. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry, Springer.","DOI":"10.1016\/B978-0-12-820045-2.00003-9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1002\/cpt.1796","article-title":"An introduction to machine learning","volume":"107","author":"Badillo","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10916-017-0703-x","article-title":"Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods","volume":"41","author":"Polat","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Singh, V., Asari, V.K., and Rajasekaran, R. (2022). A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010116"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152900","DOI":"10.1109\/ACCESS.2019.2948430","article-title":"Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ilyas, H., Ali, S., Ponum, M., Hasan, O., Mahmood, M.T., Iftikhar, M., and Malik, M.H. (2021). Chronic kidney disease diagnosis using decision tree algorithms. BMC Nephrol., 22.","DOI":"10.1186\/s12882-021-02474-z"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1007\/s00431-023-05032-x","article-title":"Symptoms for early diagnosis of chronic kidney disease in children \u2014 a machine learning\u2013based score","volume":"182","author":"Venson","year":"2023","journal-title":"Eur. J. Pediatr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1093\/jamia\/ocad097","article-title":"To predict the risk of chronic kidney disease (CKD) using Generalized Additive2 Models (GA2M)","volume":"30","author":"Lapi","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yoshizaki, Y., Kato, K., Fujihara, K., Sone, H., and Akazawa, K. (2024). Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1495054"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, W.G., Liu, X.M., Dong, Z.Y., Wang, Q., Pei, Z.Y., Chen, Y.Z., Zheng, Y., Wang, Y., Chen, P., and Feng, Z. (2022). New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.913021"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Glazyrin, Y.E., Veprintsev, D.V., Ler, I.A., Rossovskaya, M.L., Varygina, S.A., Glizer, S.L., Zamay, T.N., Petrova, M.M., Minic, Z., and Berezovski, M.V. (2020). Proteomics-Based Machine Learning Approach as an Alternative to Conventional Biomarkers for Differential Diagnosis of Chronic Kidney Diseases. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21134802"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, J.J., Tian, X., Huang, J.M., Xie, P., and Li, X.Z. (2021). The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study. BMC Nephrol., 22.","DOI":"10.1186\/s12882-021-02595-5"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lanot, A., Akesson, A., Nakano, F.K., Vens, C., Bjoerk, J., Nyman, U., Grubb, A., Sundin, P.-O., Eriksen, B.O., and Melsom, T. (2025). Enhancing individual glomerular filtration rate assessment: Can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR. BMC Nephrol., 26.","DOI":"10.1186\/s12882-025-03972-0"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/17517575.2019.1597386","article-title":"Prediction of chronic kidney disease stages by renal ultrasound imaging","volume":"14","author":"Chen","year":"2020","journal-title":"Enterp. Inform. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/nep.14376","article-title":"Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease","volume":"29","author":"Tian","year":"2024","journal-title":"Nephrology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s00467-022-05677-0","article-title":"Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves","volume":"38","author":"Weaver","year":"2023","journal-title":"Pediatr. Nephrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2417740","DOI":"10.1080\/0886022X.2024.2417740","article-title":"Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease","volume":"46","author":"Qin","year":"2024","journal-title":"Ren. Fail."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e101192","DOI":"10.1136\/bmjhci-2024-101192","article-title":"Artificial intelligence for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers","volume":"32","author":"Chang","year":"2025","journal-title":"BMJ Health Care Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.jfma.2021.08.011","article-title":"Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis","volume":"121","author":"Zhu","year":"2022","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2202755","DOI":"10.1080\/0886022X.2023.2202755","article-title":"Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease","volume":"45","author":"Chen","year":"2023","journal-title":"Ren. Fail."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s40620-023-01878-4","article-title":"Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease","volume":"37","author":"Chen","year":"2024","journal-title":"J. Nephrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1002\/mrm.26806","article-title":"Automatic renal segmentation for MR urography using 3D-GrabCut and random forests","volume":"79","author":"Yoruk","year":"2018","journal-title":"Magn. Reason. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13244-023-01370-4","article-title":"MRI texture-based machine learning models for the evaluation of renal function on different segmentations: A proof-of-concept study","volume":"14","author":"Mo","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109613","DOI":"10.1016\/j.compeleceng.2024.109613","article-title":"Classification and segmentation of kidney MRI images for chronic kidney disease detection","volume":"119","author":"Sumon","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3774423","DOI":"10.1155\/2021\/3774423","article-title":"Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases","volume":"2021","author":"Fu","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101821","DOI":"10.1016\/j.bonr.2024.101821","article-title":"Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study","volume":"24","author":"Lee","year":"2025","journal-title":"Bone Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1093\/jamia\/ocad051","article-title":"Machine-learning enhancement of urine dipstick tests for chronic kidney disease detection","volume":"30","author":"Jang","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1093\/ndt\/gfad200","article-title":"Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease","volume":"39","author":"Mavrogeorgis","year":"2024","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"140520","DOI":"10.1016\/j.ijbiomac.2025.140520","article-title":"Urine collection by sodium alginate\/CMC composite self-calibrating aerogel SERS platform for accurate screening and staging of chronic kidney disease","volume":"302","author":"Wang","year":"2025","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Iftikhar, H., Khan, M., Khan, Z., Khan, F., Alshanbari, H.M., and Ahmad, Z. (2023). A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease. Sustainability, 15.","DOI":"10.3390\/su15032754"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.ekir.2017.11.002","article-title":"Association of Pathological Fibrosis With Renal Survival Using Deep Neural Networks","volume":"3","author":"Kolachalama","year":"2018","journal-title":"Kidney Int. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mendapara, K. (2024). Development and evaluation of a chronic kidney disease risk prediction model using random forest. Front. Genet., 15.","DOI":"10.3389\/fgene.2024.1409755"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4832","DOI":"10.1038\/s41598-022-08974-8","article-title":"Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease","volume":"12","author":"Lee","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"12701","DOI":"10.1038\/s41598-023-39591-8","article-title":"Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images","volume":"13","author":"Lee","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4364","DOI":"10.1038\/s41598-025-88631-y","article-title":"Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements","volume":"15","author":"Metherall","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"015030","DOI":"10.1088\/1361-6501\/acfd4c","article-title":"Colorimetry-based and smartphone-assisted machine-learning model for quantification of urinary albumin","volume":"35","author":"Bhatt","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s00216-024-05147-6","article-title":"Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis","volume":"416","author":"Xu","year":"2024","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e295","DOI":"10.1016\/S2589-7500(20)30063-7","article-title":"A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations","volume":"2","author":"Sabanayagam","year":"2020","journal-title":"Lancet Digit Health"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/s41551-021-00745-6","article-title":"Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images","volume":"5","author":"Zhang","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/s41746-024-01393-1","article-title":"A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging","volume":"8","author":"Dong","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e55825","DOI":"10.2196\/55825","article-title":"Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach","volume":"13","author":"Bhak","year":"2025","journal-title":"JMIR Med. Inform."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"An, S., Vaghefi, E., Yang, S., Xie, L., and Squirrell, D. (2023). Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs. PLoS ONE, 18.","DOI":"10.21203\/rs.3.rs-2609919\/v1"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s12967-019-1860-0","article-title":"Comparison and development of machine learning tools in the prediction of chronic kidney disease progression","volume":"17","author":"Xiao","year":"2019","journal-title":"J. Transl. Med."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lu, Y.F., Ning, Y.C., Li, Y., Zhu, B.W., Zhang, J., Yang, Y., Chen, W., Yan, Z., Chen, A., and Shen, B. (2023). Risk factor mining and prediction of urine protein progression in chronic kidney disease: A machine learning- based study. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02269-2"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"100160","DOI":"10.1016\/j.cmpbup.2024.100160","article-title":"Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models","volume":"6","author":"Reddy","year":"2024","journal-title":"Comput. Methods Programs Biomed. Update"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shih, C.C., Chen, S.H., Chen, G.D., Chang, C.C., and Shih, Y.L. (2021). Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182312807"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/JBHI.2023.3266587","article-title":"Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease","volume":"27","author":"Liang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Bellocchio, F., Lonati, C., Titapiccolo, J.I., Nadal, J., Meiselbach, H., Schmid, M., Baerthlein, B., Tschulena, U., Schneider, M., and Schultheiss, U.T. (2021). Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD). Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182312649"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Segal, Z., Kalifa, D., Radinsky, K., Ehrenberg, B., Elad, G., Maor, G., Lewis, M., Tibi, M., Korn, L., and Koren, G. (2020). Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrol., 21.","DOI":"10.1186\/s12882-020-02093-0"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1111\/dom.15678","article-title":"Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial","volume":"26","author":"Tangri","year":"2024","journal-title":"Diabetes Obes. Metab."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13040-023-00324-2","article-title":"Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms","volume":"16","author":"Ou","year":"2023","journal-title":"BioData Min."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"898","DOI":"10.2215\/CJN.0000000000000471","article-title":"Development and External Validation of a Multidimensional Deep Learning Model to Dynamically Predict Kidney Outcomes in IgA Nephropathy","volume":"19","author":"Chen","year":"2024","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chang, H.H., Chiang, J.H., Tsai, C.C., and Chiu, P.F. (2023). Predicting hyperkalemia in patients with advanced chronic kidney disease using the XGBoost model. BMC Nephrol., 24.","DOI":"10.1186\/s12882-023-03227-w"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lu, R., Wang, S., Chen, P., Li, F., Li, P., Chen, Q., Li, X., Li, F., Guo, S., and Zhang, J. (2025). Predictive model for sarcopenia in chronic kidney disease: A nomogram and machine learning approach using CHARLS data. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1546988"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"11391","DOI":"10.1038\/s41598-025-95928-5","article-title":"Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3\u20135 and end-stage kidney disease","volume":"15","author":"Hsu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Byun, S.S., Kim, J.K., Jeong, C.W., Kwak, C., Hwang, E.C., Kang, S.H., Chung, J., Kim, Y.-J., and Ha, Y.-Z. (2024). Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02473-8"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhu, H., Qiao, S., Zhao, D., Wang, K., Wang, B., Niu, Y., Shang, S., Dong, Z., Zhang, W., and Zheng, Y. (2024). Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1390729"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1007\/s40620-024-02011-9","article-title":"External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4\u20135 chronic kidney disease","volume":"37","author":"Tran","year":"2024","journal-title":"J. Nephrol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1007\/s11255-016-1346-4","article-title":"Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models","volume":"48","author":"Chen","year":"2016","journal-title":"Int. Urol. Nephrol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2354\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T08:25:07Z","timestamp":1785831907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":77,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["diagnostics16152354"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16152354","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]}}}