[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120522-en":3,"doc-seo-120522-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120522,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Ensemble machine learning for predicting renal function decline in chronic kidney disease - development and external validation","Chronic kidney disease (CKD) is a growing global health burden, and existing predictive models often fall short in accuracy and generalizability. This study develops and externally validates an ensemble machine learning approach to improve risk prediction of renal function decline, enabling earlier and more individualized interventions. The model combines Random Forest, XGBoost, and LightGBM with feature selection and hyperparameter tuning, trained on 1,200 CKD patients and externally assessed across three medical centers.","OPEN ACCESS  \nEDITED BY  \nOlaniyi Samuel Iyiola,  \nMorgan State University, United States  \nREVIEWED BY  \nÖmer Faruk Çiçek, Selcuk University, Türkiye Samit Kumar Ghosh,  \nKhalifa University, United Arab Emirates  \n*CORRESPONDENCE  \nHong Chen  \n [yykw76@163.com](yykw76@163.com)  \nRECEIVED 22 March 2025  \nACCEPTED 25 September 2025  \nPUBLISHED 27 October 2025  \nCITATION  \nChen H, Huang Y and Chen L (2025)  \nEnsemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.  \nFront. Med. 12:1598065 .  \ndoi: 10.3389/fmed.2025.1598065  \nCOPYRIGHT  \n© 2025 Chen, Huang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 27 October 2025  \nDOI 10.3389/fmed.2025.1598065  \nEnsemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation  \nHong Chen 1*, Yuping Huang 1 and Lizhen Chen 2  \n1 Department of Nephrology, the 95th Hospital of Putian in China RongTong Medical Health Corporation, Putian, China, 2 Department of Rheumatology and Immunology, the 95th Hospital of Putian in China RongTong Medical Health Corporation, Putian, China  \nIntroduction: Chronic kidney disease (CKD) poses a significant global health challenge, requiring timely interventions to manage renal function decline. Traditional predictive models often lack accuracy and generalizability. This study aimed to develop and validate a machine learning model to enhance risk prediction of renal function decline in CKD patients, enabling early and personalized interventions. Methods: We developed an ensemble machine learning model using Random Forest, XGBoost, and LightGBM algorithms, incorporating advanced featureselection and hyperparameter tuning. The model was trained and validated on data from 1,200 CKD patients across multiple clinics, selected through stringent inclusion and exclusion criteria. Clinical, demographic, and laboratory data were processed with rigorous quality control. Model performance was assessed using area under the curve (AUC), calibration metrics, and five-fold cross-validation, with external validation across three medical centers.  \nResults: The ensemble model achieved an AUC of 0.89 (95% CI: 0.87-0.91), outperforming traditional Cox models (AUC: 0. 82, 95% CI: 0.79-0. 85) and standard machine learning approaches (AUC: 0. 85, 95% CI: 0.83-0. 87) . Key predictors identified via SHAP analysis included estimated glomerular filtration rate (eGFR), age, and urinary protein-creatinine ratio. The model demonstrated excellent calibration (slope: 0.96, 95% CI: 0.94-0.98) and robust performance across diverse patient subgroups, with a 60.6% reduction in computational resource use compared to traditional methods.  \nDiscussion: This machine learning model offers a significant advancement in predicting CKD progression, providing a reliable, generalizable tool for early risk stratification. Its superior accuracy and efficiency support integration into clinical workflows, potentially transforming CKD management by enabling proactive, data-driven interventions. Future research should focus on incorporating novel biomarkers and expanding multicenter validation to further enhance clinical applicability.  \nKEYWORDS  \nmachine learning, chronic kidney disease progression, risk prediction modeling, clinical decision support, precision nephrology  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nChronic kidney disease (CKD) has become a particularly urgent health challenge worldwide. In ","cbCaihAewCIspcKi","https://ap.wps.com/l/cbCaihAewCIspcKi","pdf",1555345,1,16,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What problem does the study address in chronic kidney disease (CKD)?\",\"answer\":\"The study targets the need for timely and accurate prediction of renal function decline in CKD. It addresses limitations of traditional models, especially reduced accuracy and limited generalizability across patient groups.\"},{\"question\":\"How was the ensemble model developed and validated?\",\"answer\":\"The model was built using Random Forest, XGBoost, and LightGBM with feature selection and hyperparameter tuning. Training used data from 1,200 CKD patients, and external validation was performed across three medical centers using performance metrics such as AUC, calibration, and five-fold cross-validation.\"},{\"question\":\"Which predictors were most important and how well did the model perform?\",\"answer\":\"SHAP analysis identified eGFR, age, and urinary protein-creatinine ratio as key predictors. The ensemble model achieved AUC 0.89 and showed good calibration, outperforming traditional Cox models and standard machine learning approaches.\"}]","Ensemble machine learning for predicting renal function decline in chronic kidney disease - development and external validation | PDF",1785730479,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ensemble-machine-learning-for-predicting-renal-function-decline-in-chronic-kidney-disease-development-and-external-validation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ensemble-machine-learning-for-predicting-renal-function-decline-in-chronic-kidney-disease-development-and-external-validation/120522/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in chronic kidney disease (CKD)?","Question",{"text":75,"@type":76},"The study targets the need for timely and accurate prediction of renal function decline in CKD. It addresses limitations of traditional models, especially reduced accuracy and limited generalizability across patient groups.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the ensemble model developed and validated?",{"text":80,"@type":76},"The model was built using Random Forest, XGBoost, and LightGBM with feature selection and hyperparameter tuning. Training used data from 1,200 CKD patients, and external validation was performed across three medical centers using performance metrics such as AUC, calibration, and five-fold cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which predictors were most important and how well did the model perform?",{"text":84,"@type":76},"SHAP analysis identified eGFR, age, and urinary protein-creatinine ratio as key predictors. 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