{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:22:30Z","timestamp":1792790550006,"version":"4.5.0"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Chronic Kidney Disease (CKD) is a prevalent health condition that requires personalized treatment planning at each of its five stages. Machine Learning (ML) and Generative AI have shown promise in predicting CKD progression based on patient data. However, existing prediction models have limitations on generalizability, interpretability, and resource requirements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>This study aims to develop a clinical support system using ML models to classify CKD stages accurately. The research focuses on feature selection strategies and model performance evaluation to enhance prediction accuracy and guide personalized treatment planning for CKD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The study utilizes ML algorithms, including Gradient Boosting, XGBoost, CatBoost, and GAN AML, to categorize CKD stages. Various feature selection techniques such as Recursive Feature Elimination, chi-square test, and SHAP are employed to identify relevant features for improved prediction accuracy. The models are evaluated based on precision, recall, F1-score, accuracy, and AUC-ROC metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The findings demonstrate the effectiveness of CatBoost and GAN AML in accurately classifying CKD stages, highlighting the importance of expert knowledge in selecting feature selection strategies to enhance ML model performance. Future research directions include validating diverse datasets, integrating with clinical practice, and improving interpretability and explainability in CKD prediction models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329251316447","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T03:25:27Z","timestamp":1740540327000},"page":"1959-1987","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Clinical decision system for chronic kidney disease staging using machine learning"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8991-708X","authenticated-orcid":false,"given":"E.","family":"Chandralekha","sequence":"first","affiliation":[{"name":"Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9085-9470","authenticated-orcid":false,"given":"T. R.","family":"Saravanan","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7450-4963","authenticated-orcid":false,"given":"N.","family":"Vijayaraj","sequence":"additional","affiliation":[{"name":"Department of CSE, Vel Tech Rangarajan Dr Sagunthala R&amp;D Institute of Science and Technology, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.04.036"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.17148\/IJARCCE.2018.71021"},{"key":"e_1_3_3_4_2","article-title":"A comprehensive analysis of detecting chronic kidney disease by employing machine learning algorithms","volume":"7","author":"Nishat MM","year":"2022","unstructured":"Nishat MM, Faisal F, Dip RR, et al. A comprehensive analysis of detecting chronic kidney disease by employing machine learning algorithms. EAI Endorsed Trans Pervas Health Technol 2022; 7: e1.","journal-title":"EAI Endorsed Trans Pervas Health Technol"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12010212"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-240431"},{"key":"e_1_3_3_7_2","first-page":"2350005","article-title":"PIRAP: Medical cancer rehabilitation healthcare centre data maintenance based on IoT-based deep federated collaborative learning","volume":"33","author":"Pradhan MR","year":"2023","unstructured":"Pradhan MR. PIRAP: Medical cancer rehabilitation healthcare centre data maintenance based on IoT-based deep federated collaborative learning. Int J Coop Inf Syst 2023; 33: 2350005.","journal-title":"Int J Coop Inf Syst"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-237442"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3266587"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2995310"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2911071"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3133700"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3312183"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3114306"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3264270"},{"key":"e_1_3_3_16_2","first-page":"100","article-title":"Detection of chronic kidney disease using machine learning algorithms with the least number of predictors","volume":"10","author":"Almasoud M","year":"2019","unstructured":"Almasoud M, Ward TE. Detection of chronic kidney disease using machine learning algorithms with the least number of predictors. Int J Soft Comput Appl 2019; 10: 100\u2013110.","journal-title":"Int J Soft Comput Appl"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111316"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2021.3073629","article-title":"Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening","volume":"9","author":"Rashed-Al-Mahfuz M","year":"2021","unstructured":"Rashed-Al-Mahfuz M, Haque A, Azad A, et al. Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening. 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