{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:20:26Z","timestamp":1767651626675,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"crossref","award":["AB24010085","AB23026120","AA24263010"],"award-info":[{"award-number":["AB24010085","AB23026120","AA24263010"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62462019","62561018"],"award-info":[{"award-number":["62462019","62561018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2023A1515012846"],"award-info":[{"award-number":["2023A1515012846"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Laboratory of Equipment Data Security and Guarantee Technology, Ministry of Education","award":["GDZB2024060500"],"award-info":[{"award-number":["GDZB2024060500"]}]},{"DOI":"10.13039\/100012547","name":"Natural Science Foundation of Guangxi Zhuang Autonomous Region","doi-asserted-by":"crossref","award":["2025GXNSFBA069410"],"award-info":[{"award-number":["2025GXNSFBA069410"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Basic Scientific Research Capacity Enhancement Program for Young and Middle-Aged Teachers in Guangxi Institutions of Higher Education","award":["2024KY0233","2025KY0243"],"award-info":[{"award-number":["2024KY0233","2025KY0243"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Chronic kidney disease (CKD) impacts over 850 million people globally, representing a critical public health issue, yet existing risk assessment methodologies inadequately address the complexity of disease progression trajectories. Traditional machine learning approaches encounter critical limitations including inefficient hyperparameter selection and lack of clinical transparency, hindering their deployment in healthcare settings. This study introduces an innovative computational framework that integrates adaptive Multi-Armed Bandit (MAB) strategies with BorderlineSMOTE sampling techniques to improve CKD risk assessment. The proposed methodology leverages XGBoost within an ensemble learning paradigm enhanced by Upper Confidence Bound exploration strategy, coupled with a comprehensive interpretability system incorporating SHAP and LIME analytical tools to ensure model transparency. To address the challenge of algorithmic interpretability while maintaining clinical utility, a four-level risk categorization framework was developed, employing cross-validated stratification methods and balanced performance evaluation metrics, thereby ensuring fair predictive accuracy across diverse patient populations and minimizing bias toward dominant risk categories. Through rigorous empirical evaluation on clinical datasets, we performed extensive comparative analysis against sixteen established algorithms using paired statistical testing with Bonferroni correction. The MAB-optimized framework achieved superior predictive performance with accuracy of 91.8%, F1-score of 91.0%, and ROC-AUC of 97.8%, demonstrating superior performance within the evaluated cohort of reference algorithms (p-value &lt; 0.001). Remarkably, our optimized framework delivered nearly ten-fold computational efficiency gains relative to conventional grid search methods while preserving robust classification performance. Feature importance analysis identified albumin-to-creatinine ratio, eGFR measurements, and CKD staging as dominant prognostic factors, demonstrating concordance with established clinical nephrology practice. This research addresses three core limitations in healthcare artificial intelligence: optimization computational cost, model interpretability, and consistent performance across heterogeneous clinical populations, offering a practical solution for improved CKD risk stratification in clinical practice.<\/jats:p>","DOI":"10.3390\/sym17111808","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T11:47:19Z","timestamp":1761652039000},"page":"1808","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Armed Bandit Optimization for Explainable AI Models in Chronic Kidney Disease Risk Evaluation"],"prefix":"10.3390","volume":"17","author":[{"given":"Jianbo","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Application, Guilin University of Technology, Guilin 541006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7693-9722","authenticated-orcid":false,"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Equipment Data Security and Guarantee Technology, Ministry of Education, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2628-8471","authenticated-orcid":false,"given":"Jia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Application, Guilin University of Technology, Guilin 541006, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahmed, K., Dubey, M.K., Dubey, S., and Pandey, D.K. 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