[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120475-en":3,"doc-seo-120475-105":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":4,"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},120475,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Predicting chronic kidney disease using supervised machine learning","Chronic kidney disease (CKD) is a serious global health issue that often advances silently until irreversible damage occurs. This study develops and evaluates eight supervised machine learning classifiers to predict CKD status from routine clinical and laboratory features. Using a publicly available dataset of 400 patients, the work applies imputation, encoding, and normalization, then compares models with accuracy, precision, recall, and F1-score. XGBoost and Extra Trees deliver the best accuracy (98.3%), while feature importance highlights albumin, hemoglobin, blood urea, and serum creatinine as key predictors, supporting interpretable clinical decision support.","Open Access  \nInternational Medical Science Research Journal ISSN 2707-3394 (Print), ISSN 2707-3408 (Online) Fair East Publishers  \n[www.fepbl.com](www.fepbl.com)  \nPredicting chronic kidney disease using supervised machine learning  \nEmmanuel Adu Sarfo 1, Harold Tobias Adu-Twum2, Philip Mensah3, Michael Nti Ababio4, Dr Yejide Lamina5, & Dr. Yewande Iyimide Adeyeye6  \n1Youngstown State University, Department of Mathematics and Statistics, USA 2Youngstown State University, Department of Mathematics and Statistics, USA 3University of Minnesota, Office of Information Technology, USA  \n4Department of Community and Regional Planning, University of Nebraska-Lincoln, NE, USA  \n5Lagos State University College of Medicine, Lagos, Nigeria  \n6GP Registrar, Dumfries and Galloway Royal Infirmary Dumfries, Scotland  \nCorresponding Author: Emmanuel Adu Sarfo  \nCorresponding Author Email: [eadusarfo@student.ysu.edu](eadusarfo@student.ysu.edu)  \nArticle Info  \nReceived: 20-03-25  \nAccepted: 28-06-25  \nPublished: 11-07-25  \nVolume: 5  \nIssue: 5  \nPage No: 195-201  \nLicensing Details:  \nAuthor retains the right of this article. The article is distributed under the terms of the Creative Commons Attribution Non  \nCommercial 4.0 Licence  \nAbstract  \nChronic kidney disease (CKD) is a serious global health issue that often progresses without symptoms until irreversible damage has occurred. In this study, we develop and evaluate multiple machine learning models to predict CKD status using routine clinical and laboratory features. Using a publicly available dataset of 400 patients, we implement eight supervised classification algorithms, including Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Stochastic Gradient Boosting, XGBoost, Extra Trees, and K-Nearest Neighbors (KNN) . The data were preprocessed through imputation, encoding, and normalization, and models were assessed using accuracy, precision, recall, and F1-score. XGBoost and Extra Trees achieved the highest predictive accuracy (98.3%), followed closely by other ensemble methods. Feature importance analyses consistently identified albumin, hemoglobin, blood urea, and serum creatinine as the most predictive variables. Our findings highlight the utility of ensemble learning techniques for accurate and interpretable CKD prediction, suggesting their potential application in clinical decision support tools.  \nKeywords: Chronic Kidney Disease, Machine Learning, Classification, XGBoost, Feature Importance, Medical Diagnostics, Ensemble Methods, Clinical Decision Support.  \nDOI: 10.51594/imsrj.v5i5 .1967  \nDOI URL: [https://doi.org/10.51594/imsrj.v5i5.1967](https://doi.org/10.51594/imsrj.v5i5.1967)  \nINTRODUCTION  \nChronic kidney disease (CKD) is a critical global health issue, affecting nearly 10 % of the population worldwide and contributing significantly to cardiovascular complications, premature mortality, and escalating healthcare costs (Li et al., 2020; Tonelli & Riella, 2016) . The disease is characterized by a progressive and often asymptomatic decline in kidney function, which, if undetected in early stages, can advance to end-stage renal disease requiring dialysis or transplantation (KDIGO, 2012; Levey et al., 2003) . Clinical guidelines emphasize the role of regular screening, especially among at-risk populations including individuals with diabetes, hypertension, or a family history of kidney disorders (KDIGO, 2012) . However, universal screening is neither practical nor cost-effective in many settings, particularly in regions where health risks are compounded by environmental and infrastructural constraints (Adu Sarfo & Tweneboah, 2024) . This has sparked interest in leveraging machine learning to identify high-risk individuals based on routinely collected clinical and lifestyle data (Islam et al., 2023; Saif et al., 2024) . Predictive models offer a data-driven strategy to support early diagnosis, enabling timely interventions and better resource allocation (Saif et al., 2024)","cbCaieX53FTeX4Vt","https://ap.wps.com/l/cbCaieX53FTeX4Vt","pdf",401048,1,7,"English","en",105,"# Abstract\n# Introduction\n# Dataset and Preprocessing\n# Data Cleaning and Handling Missing Values","[{\"question\":\"Which supervised machine learning models were used to predict CKD status?\",\"answer\":\"Eight supervised classifiers were implemented: Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Stochastic Gradient Boosting, XGBoost, Extra Trees, and K-Nearest Neighbors (KNN).\"},{\"question\":\"How were missing values handled in the dataset?\",\"answer\":\"Numerical missing entries were imputed using the mean value, categorical features were filled with the mode, and records with excessive missingness across key fields were excluded.\"},{\"question\":\"What features were identified as most predictive for CKD?\",\"answer\":\"Feature importance analyses consistently indicated albumin, hemoglobin, blood urea, and serum creatinine as the most predictive variables for CKD.\"}]","Predicting chronic kidney disease using supervised machine learning | 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supervised machine learning models were used to predict CKD status?","Question",{"text":75,"@type":76},"Eight supervised classifiers were implemented: Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Stochastic Gradient Boosting, XGBoost, Extra Trees, and K-Nearest Neighbors (KNN).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were missing values handled in the dataset?",{"text":80,"@type":76},"Numerical missing entries were imputed using the mean value, categorical features were filled with the mode, and records with excessive missingness across key fields were excluded.",{"name":82,"@type":73,"acceptedAnswer":83},"What features were identified as most predictive for CKD?",{"text":84,"@type":76},"Feature importance analyses consistently indicated albumin, hemoglobin, blood urea, and serum creatinine as the most predictive variables for 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