[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122064-en":3,"doc-seo-122064-105":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},122064,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms for CKD Risk Prediction","Chronic Kidney Disease (CKD) is a major global health challenge that affects patients’ quality of life and requires timely risk identification for earlier intervention. This study provides a comparative analysis of eight traditional machine learning models and three deep learning approaches for CKD risk prediction. Models are evaluated using metrics including accuracy, precision, recall, and F1 score, as well as computational efficiency, on three dataset variants generated with balancing, original imbalance, and feature selection. Results indicate minimal performance differences across dataset versions and highlight RF, SVM, AdaBoost, and XGBoost for lower training and testing runtime. Neural network models show no performance gain and train more slowly due to limited data availability in the original dataset.","Bond University Research Repository  \nComparative Analysis of Machine Learning Algorithms for CKD Risk Prediction  \nYang, Weilin; Ahmed, Nasim; Barczak, Andre L.C.  \nPublished in: IEEE Access  \nDOI:  \n10.1109/ACCESS.2024.3499355  \nLicence:  \nCC BY-NC-ND  \nLink to output in Bond University research repository.  \nRecommended citation(APA):  \nYang, W. , Ahmed, N. , & Barczak, A. L. C. (2024) . Comparative Analysis of Machine Learning Algorithms for CKD Risk Prediction. IEEE Access, 12, 171205-171220 . [https://doi.org/10.1109/ACCESS.2024.3499355](https://doi.org/10.1109/ACCESS.2024.3499355)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nFor more information, or if you believe that this document breaches copyright, please contact the Bond University research repository coordinator.  \nDownload date: 03 Aug 2026  \nReceived 30 August 2024, accepted 7 November 2024, date of publication 15 November 2024, date of current version 26 November 2024.  \nDigital Object Identifier 10.1109/ACCESS.2024.3499355  \nComparative Analysis of Machine Learning Algorithms for CKD Risk Prediction  \nWEILIN YANG1, NASIM AHMED1,(Senior Member, IEEE), AND ANDRE L. C. BARCZAK2,(Senior Member, IEEE)  \n1 School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia  \n2Centre for Data Analytics, Bond University, Gold Coast, QLD 4226, Australia Corresponding author: Nasim Ahmed ([nasim.ahmed@sydney.edu.au](nasim.ahmed@sydney.edu.au))  \nABSTRACT Chronic Kidney Disease (CKD) remains a significant global health challenge, with increasing prevalence and a substantial impact on patient quality of life. Early and accurate prediction of CKD risk is crucial for timely intervention and management. This study presents a comprehensive comparative analysis of both machine learning and deep learning algorithms applied to predict CKD risk. The research involved the application of eight traditional machine learning algorithms: Naive Bayes, K-nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, AdaBoost, and XGBoost, each implemented on a CKD dataset retrieved from the UCI data repository. Furthermore, three neural networkbased algorithms, Artificial Neural Network (ANN), Simple Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were used to compare to the traditional algorithms. This comparative study not only assessed each algorithm’s performance in terms of accuracy, precision, recall, and F1 score but also examined their computational efficiency and applicability in real-world clinical settings. All eleven algorithms were trained with three versions of the dataset. The first version kept the original unbalance between classes and used KNN imputation to fill up missing values (unbalanced). The second dataset used SMOTENC to create new samples to balance the dataset (balanced) . The third dataset used feature selection to choose 14 features from the original 24 . The results showed that there is almost no performance difference among the classifiers produced with the balanced, unbalanced and feature selection datasets. This means that the best algorithms for this task are the ones with short training and testing runtime, namely RF, SVM, AdaBoost and XGBoost. The experiments also showed that the neural network-based algorithms had no performance advantage and were slower to train due to the small size of samples available in the original dataset.  \nINDEX TERMS Chronic kidney disease, machine learning, deep learning, risk prediction, healthcare analytics.  \nI. INTRODUCTION  \nKidneys are vital organs for filtering waste and excess fluids from the blood, regulating blood pressure, and maintaining the balance of important minerals in the body [1] . As of late 2","cbCaicVjzfOibelr","https://ap.wps.com/l/cbCaicVjzfOibelr","pdf",2379993,1,17,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"How many machine learning and deep learning algorithms are compared for CKD risk prediction?\",\"answer\":\"The study compares eight traditional machine learning algorithms and three neural-network-based deep learning algorithms, for a total of eleven models.\"},{\"question\":\"What three versions of the CKD dataset are used in the experiments?\",\"answer\":\"One version keeps the original class imbalance and imputes missing values with KNN; the second balances classes using SMOTENC; the third uses feature selection to reduce features from 24 to 14.\"},{\"question\":\"Which algorithms perform best in terms of practicality, and why?\",\"answer\":\"RF, SVM, AdaBoost, and XGBoost are identified as the best choices because they have short training and testing runtime, while overall predictive performance differences across dataset variants are negligible.\"}]","Comparative Analysis of Machine Learning Algorithms for CKD Risk Prediction | PDF",1785808644,43,{"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},"comparative-analysis-of-machine-learning-algorithms-for-ckd-risk-prediction","",{"@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/comparative-analysis-of-machine-learning-algorithms-for-ckd-risk-prediction/122064/",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","2026-08-04",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},"How many machine learning and deep learning algorithms are compared for CKD risk prediction?","Question",{"text":75,"@type":76},"The study compares eight traditional machine learning algorithms and three neural-network-based deep learning algorithms, for a total of eleven models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What three versions of the CKD dataset are used in the experiments?",{"text":80,"@type":76},"One version keeps the original class imbalance and imputes missing values with KNN; the second balances classes using SMOTENC; the third uses feature selection to reduce features from 24 to 14.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithms perform best in terms of practicality, and why?",{"text":84,"@type":76},"RF, SVM, AdaBoost, and XGBoost are identified as the best choices because they have short training and testing runtime, while overall predictive performance differences across dataset variants are negligible.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]