[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122616-en":3,"doc-seo-122616-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122616,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","The Performance of Machine Learning for Chronic Kidney Disease Diagnosis","This paper reviews the performance of machine learning (ML) models and develops approaches for automated chronic kidney disease diagnosis. Selecting an appropriate, well-performing ML model is emphasized to improve diagnostic precision and accuracy. The study applies the Joana Briggs Institute (JBI) scoping review methodology, using journal indexing and publication year as inclusion and exclusion criteria. Findings indicate current detection focuses on ensemble-based and deep-learning methods, with deep learning reaching up to 99.75% accuracy.","The Performance of Machine Learning for Chronic Kidney Disease Diagnosis  \nTsehay Admassu Assegie 1,*, Yenework Belayneh Chekol2  \n1Department of Computer Science, Injibara University, Injibara, Ethiopia  \n2Department of Information Technology, Injibara University, Injibara, Ethiopia  \nReceived 16 December 2022; received in revised form 19 January 2023; accepted 04 March 2023  \nDOI: [https://doi.org/10.46604/emsi.2023.11285](https://doi.org/10.46604/emsi.2023.11285)  \nAbstract  \nThis paper aims to review the performance of different machine learning (ML) models and develop models for the automated diagnosis of chronic kidney disease. To detect chronic kidney disease with better precision, selecting the right and better-performing ML model is significant as it improves the precision and accuracy of the chronic kidney disease diagnosis. The study uses the Joana Briggs Institute (JBI) scoping review methodology, which involves different steps such as searching relevant literature, conducting the review, and reporting the review result. In the search, the year of publication and the indexing of journals where the studies are published is used as inclusion and exclusion criteria. The review result shows that the current chronic kidney disease detection has focused on the development of ensemble-based and deep-learning methods. The deep learning method can achieve a higher accuracy of 99.75% .  \nKeywords: chronic kidney disease, machine learning, performance, scoping review  \n1. Introduction  \nChronic kidney disease is one of the deadliest diseases on the globe if not treated early. According to Shanmugarajeshwari and Ilayaraja [1], chronic kidney disease affects 10% of the world’s population. In chronic kidney disease detection, the complexity of differentiating symptoms or common individual factors can be used to identify the disease at the early stages during the diagnosis. The researchers have developed different machine learning (ML) models, to aid human experts in automating the detection of chronic kidney disease at the early stages of its occurrence. In the traditional method, a urine test is used to identify whether a patient is suffering from kidney disease or not. In the urine test, medical experts examine urine albumin levels to detect kidney disease. However, the urine test has some weaknesses, such as the albumin level of the urine test can be normal in the early stages, the condition of equipment, and the highly experienced medical experts are required for accurate detection of chronic kidney disease.  \nAlternative chronic kidney disease examination techniques with the help of an automated ML model have been widely proposed by numerous researchers. The use of automated ML detection ranges from simple linear models such as support vector machines and tree-based models to complex deep neural networks and ensemble learning methods. In addition, the research on automated detection of chronic kidney disease has attracted many researchers from the field of artificial intelligence (ANN) especially, data mining and ML. For instance, Amirgaliyev et al. [2] analyzed the performance of logistic regression (LR), support vector machine (SVM), and ANN for chronic kidney disease detection. The comparative result shows that better accuracy of 94.60% is achieved using the SVM model. The model was developed with the chronic kidney disease data collected from the University of California Irvine (UCI) repository.  \n* Corresponding author. E-mail address: [tsehayadmassu2006@gmail.com](tsehayadmassu2006@gmail.com)  \n[Tel.:](Tel.:) +251-9-77340351  \nSimilarly, Saringat et al. [3] applied the ensemble method to develop an automated kidney disease detection model. In the study, various features of chronic kidney disease are used to train the model to improve its accuracy. The performance of the developed Naive Bayes (NB) model shows that the ensemble method detects the disease with an accuracy of 98.5% using the chronic kidney disease dataset","cbCailVU91zKvktk","https://ap.wps.com/l/cbCailVU91zKvktk","pdf",273794,1,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What is the main goal of the paper on chronic kidney disease diagnosis?\",\"answer\":\"The paper aims to review the performance of different machine learning models and develop models for automated chronic kidney disease diagnosis, improving precision and accuracy.\"},{\"question\":\"What methodology is used to conduct the review?\",\"answer\":\"The study uses the Joana Briggs Institute (JBI) scoping review methodology, including literature search, review execution, and reporting. Publication year and journal indexing guide inclusion and exclusion.\"},{\"question\":\"What do the review results suggest about current chronic kidney disease detection approaches?\",\"answer\":\"Current detection largely focuses on ensemble-based and deep-learning methods. Deep learning is reported to achieve high accuracy, reaching 99.75%.\"}]","The Performance of Machine Learning for Chronic Kidney Disease Diagnosis | PDF",1785811742,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"the-performance-of-machine-learning-for-chronic-kidney-disease-diagnosis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/the-performance-of-machine-learning-for-chronic-kidney-disease-diagnosis/122616/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the paper on chronic kidney disease diagnosis?","Question",{"text":74,"@type":75},"The paper aims to review the performance of different machine learning models and develop models for automated chronic kidney disease diagnosis, improving precision and accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What methodology is used to conduct the review?",{"text":79,"@type":75},"The study uses the Joana Briggs Institute (JBI) scoping review methodology, including literature search, review execution, and reporting. Publication year and journal indexing guide inclusion and exclusion.",{"name":81,"@type":72,"acceptedAnswer":82},"What do the review results suggest about current chronic kidney disease detection approaches?",{"text":83,"@type":75},"Current detection largely focuses on ensemble-based and deep-learning methods. 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