{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:59:23Z","timestamp":1791518363508,"version":"4.3.4"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"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>Background\/Objectives: Chronic kidney disease (CKD) remains underdiagnosed until advanced stages, motivating reliable, clinically deployable screening models that pair high discrimination with an explicit operating threshold and transparent explanations. Methods: In this study, we propose a CKD detection framework that integrates structured preprocessing, class imbalance handling, stratified 10-fold cross-validation with out-of-fold (OOF) prediction, and clinically oriented threshold selection via the Youden index, followed by explainability using SHAP and LIME. Experiments were conducted on two datasets. Across a broad panel of ten machine learning models, gradient boosting methods consistently dominated. Results: LightGBM achieved the best overall clinical composite performance on both datasets. On Dataset 1, LightGBM delivered near-ceiling OOF discrimination (ROC-AUC = 99.98, PR-AUC = 99.98) and an excellent clinically balanced performance at the best Youden threshold (0.41), reaching sensitivity = 99.20, specificity = 99.60, accuracy = 99.40, F1 = 99.40, and MCC = 98.80, with robust cross-validation stability (CV AUC = 99.99 \u00b1 0.04; CV sensitivity = 99.10 \u00b1 1.81; CV specificity = 99.46 \u00b1 1.42; CV MCC = 98.59 \u00b1 2.19), strong calibration (Brier = 0.006), and fast training (0.078 \u00b1 0.019 s\/fold). On Dataset 2, LightGBM maintained high generalization (ROC-AUC = 99.72, PR-AUC = 99.64) and clinically deployable balance at the best Youden threshold (0.35), achieving sensitivity = 98.10, specificity = 98.03, accuracy = 98.06, F1 = 98.06, and MCC = 96.13, with consistent fold-wise performance (CV AUC = 99.69 \u00b1 0.25; CV sensitivity = 97.25 \u00b1 1.25; CV specificity = 98.11 \u00b1 1.02; CV MCC = 95.37 \u00b1 1.56), acceptable calibration (Brier = 0.0173), and practical training time (0.742 \u00b1 0.144 s\/fold). Conclusions: Finally, SHAP and LIME explanations confirmed that model decisions align with clinically meaningful renal function and symptom\/biomarker patterns at both population and patient levels, supporting safer translation of the proposed framework into CKD screening and decision-support workflows.<\/jats:p>","DOI":"10.3390\/diagnostics16132000","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T02:16:44Z","timestamp":1782699404000},"page":"2000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9947-5393","authenticated-orcid":false,"given":"Abass","family":"Al-Momany","sequence":"first","affiliation":[{"name":"Department of Medical Laboratory Sciences, The University of Jordan, Queen Rania St, Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3160-6542","authenticated-orcid":false,"given":"Omar","family":"Almomani","sequence":"additional","affiliation":[{"name":"Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1032-627X","authenticated-orcid":false,"given":"Ensaf Y.","family":"Almomani","sequence":"additional","affiliation":[{"name":"Department of Basic Medical Sciences, Faculty of Medicine, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1111\/j.1523-1755.2005.00365.x","article-title":"Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO)","volume":"67","author":"Levey","year":"2005","journal-title":"Kidney Int."},{"key":"ref_2","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_3","first-page":"10053","article-title":"Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980\u20132015: A systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"Hernandez","year":"2016","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S191","DOI":"10.2337\/dc23-S011","article-title":"Chronic kidney disease and risk management: Standards of care in diabetes","volume":"46","author":"ElSayed","year":"2023","journal-title":"Diabetes Care"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Segal, Z., Kalifa, D., Radinsky, K., Ehrenberg, B., Elad, G., Maor, G., Lewis, M., Tibi, M., and Korn, L. (2020). Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrol., 21.","DOI":"10.1186\/s12882-020-02093-0"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"11862","DOI":"10.1038\/s41598-019-48263-5","article-title":"Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning","volume":"9","author":"Makino","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chowdhury, N.H., Reaz, M.B.I., Haque, F., Ahmad, S., Ali, S.H.M., A Bakar, A.A., and Bhuiyan, M.A.S. (2021). Performance analysis of conventional machine learning algorithms for identification of chronic kidney disease in type 1 diabetes mellitus patients. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122267"},{"key":"ref_9","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_10","first-page":"200397","article-title":"On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence","volume":"22","author":"Dharmarathne","year":"2024","journal-title":"Intell. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.kisu.2021.11.003","article-title":"Epidemiology of chronic kidney disease: An update 2022","volume":"12","author":"Kovesdy","year":"2022","journal-title":"Kidney Int. Suppl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Molinara, M., Cancelliere, R., Di Tinno, A., Ferrigno, L., Shuba, M., Kuzhir, P., Maffucci, A., and Micheli, L. (2022). A deep learning approach to organic pollutants classification using voltammetry. Sensors, 22.","DOI":"10.3390\/s22208032"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sayed, A., and Hemdan, E.E.-D. (2024). Conditional generative adversarial networks with optimized machine learning for fault detection of triplex pump in industrial digital twin. Processes, 12.","DOI":"10.3390\/pr12112357"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s44163-025-00770-3","article-title":"A comparative study of the diabetes progression prediction techniques","volume":"6","author":"Abualhaj","year":"2025","journal-title":"Discov. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jabali, E., Almomani, O., Qatawneh, L., Badwan, S., Almomani, Y., Al-Soreeky, M., Ibrahim, A., and Khalil, N. (2026). Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis. J. Imaging, 12.","DOI":"10.3390\/jimaging12030121"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e14424","DOI":"10.1111\/nep.14424","article-title":"Artificial intelligence to predict chronic kidney disease progression to kidney failure: A narrative review","volume":"30","author":"Miller","year":"2025","journal-title":"Nephrology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1039\/D4DD00318G","article-title":"Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses","volume":"4","author":"Cancelliere","year":"2025","journal-title":"Digit. Discov."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lei, N., Zhang, X., Wei, M., Lao, B., Xu, X., Zhang, M., Chen, H., Xu, Y., Xia, B., and Zhang, D. (2022). Machine learning algorithms\u2019 accuracy in predicting kidney disease progression: A systematic review and meta-analysis. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-01951-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s42979-025-04264-y","article-title":"Automated diagnosis of dental diseases using deep learning on radiographic images","volume":"6","author":"Tarek","year":"2025","journal-title":"SN Comput. Sci."},{"key":"ref_20","first-page":"e13091","article-title":"Pd_ebm: An integrated boosting approach based on selective features for unveiling parkinson\u2019s disease diagnosis with global and local explanations","volume":"7","author":"Khanom","year":"2025","journal-title":"Eng. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Owida, H.A., Al-Nabulsi, J., Al Hawamdeh, N., Abuowaida, S., and Elsoud, E.A. (2024). Deep Learning-Based Kidney Disease Classification Using ResNet50: Enhancing Diagnostic Accuracy. Proceedings of the 2024 Second Jordanian International Biomedical Engineering Conference (JIBEC), Amman, Jordan, 27\u201328 November 2024, IEEE.","DOI":"10.1109\/JIBEC63210.2024.10931915"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kausar, M.A., Ghazal, T.M., Al Batahari, M., Nasar, M., Al-Dmour, N.A., and Saeed, A.Q. (2025). Kidney Disease Prediction Using Machine Learning. Proceedings of the 2025 3rd International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, 1\u20132 May 2025, IEEE.","DOI":"10.1109\/ICBATS66542.2025.11258099"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1016\/j.csbj.2022.06.003","article-title":"Application of interpretable machine learning for early prediction of prognosis in acute kidney injury","volume":"20","author":"Hu","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_24","first-page":"1886","article-title":"Clinical applications of machine learning algorithms: Beyond the black box","volume":"364","author":"Watson","year":"2019","journal-title":"BMJ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.tig.2020.03.005","article-title":"Opening the black box: Interpretable machine learning for geneticists","volume":"36","author":"Azodi","year":"2020","journal-title":"Trends Genet."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Peng, J., and Bouridane, A. (2023). Survey of explainable AI techniques in healthcare. Sensors, 23.","DOI":"10.3390\/s23020634"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e70018","DOI":"10.1002\/widm.70018","article-title":"Unveiling explainable AI in healthcare: Current trends, challenges, and future directions","volume":"15","author":"Noor","year":"2025","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e70060","DOI":"10.1111\/exsy.70060","article-title":"From AI to the era of explainable AI in Healthcare 5.0: Current state and future outlook","volume":"42","author":"Rahman","year":"2025","journal-title":"Expert Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e72172","DOI":"10.1002\/hsr2.72172","article-title":"Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions","volume":"9","author":"Shiddik","year":"2026","journal-title":"Health Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7378307","DOI":"10.1155\/2022\/7378307","article-title":"Comparison of different machine learning techniques to predict diabetic kidney disease","volume":"2022","author":"David","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arif, M.S., Rehman, A.U., and Asif, D. (2024). Explainable machine learning model for chronic kidney disease prediction. Algorithms, 17.","DOI":"10.3390\/a17100443"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3687","DOI":"10.1038\/s41598-024-54375-4","article-title":"Investigation on explainable machine learning models to predict chronic kidney diseases","volume":"14","author":"Ghosh","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100398","DOI":"10.1016\/j.health.2025.100398","article-title":"A deep learning framework for chronic kidney disease stage classification","volume":"7","author":"Hegde","year":"2025","journal-title":"Healthc. Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Iftikhar, H., Hashem, A.F., and Rodrigues, P.C. (2025). Clinical application of machine learning models for early-stage chronic kidney disease detection. Diagnostics, 15.","DOI":"10.3390\/diagnostics15202610"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"396","DOI":"10.4258\/hir.2025.31.4.396","article-title":"Deep Learning-Based Death Prediction Model for Chronic Kidney Disease","volume":"31","author":"Kim","year":"2025","journal-title":"Healthc. Inform. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, J., Li, L., and Chen, J. (2025). Bayesian optimization meets explainable AI: Enhanced chronic kidney disease risk assessment. Mathematics, 13.","DOI":"10.3390\/math13172726"},{"key":"ref_38","unstructured":"Rubini, P.S.L., and Eswaran, P. (2015). Chronic Kidney Disease, UCI Machine Learning Repository."},{"key":"ref_39","unstructured":"Kharoua, R.E. (2026, June 22). Chronic Kidney Disease Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/rabieelkharoua\/chronic-kidney-disease-dataset-analysis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s11255-024-04281-5","article-title":"Machine learning approaches for predicting and diagnosing chronic kidney disease: Current trends, challenges, solutions, and future directions","volume":"57","author":"Gogoi","year":"2025","journal-title":"Int. Urol. Nephrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSWIS.407404","article-title":"Semantic-Enhanced Classification of DNA Sequences Using Machine Learning and Deep Learning Approaches","volume":"22","author":"Salloum","year":"2026","journal-title":"Int. J. Semant. Web Inf. Syst. (IJSWIS)"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Shambour, Q., Al-Zyoud, M., and Almomani, O. (2025). Quantum-inspired hybrid metaheuristic feature selection with SHAP for optimized and explainable spam detection. Symmetry, 17.","DOI":"10.3390\/sym17101716"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2000\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T02:52:58Z","timestamp":1782701578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/13\/2000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,26]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16132000"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16132000","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,26]]}}}