[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290802-105":59,"doc-detail-290802-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","classification-of-chronic-kidney-disease-based-on-machine-learning-techniques-abstract","Classification of chronic kidney disease based on machine learning techniques - Abstract","","Paper proposes an ML-based pipeline to predict chronic kidney disease (CKD) by leveraging patient health dataset information and evaluating nine distinct algorithms: random forest, Naïve Bayes, support vector machine, decision tree, logistic regression, XGBoost, AdaBoost, k-nearest neighbors, and neural network. Experiments compare models using F1-score, precision, accuracy, recall, and run time. Findings show AdaBoost achieves the best performance with 99.17% accuracy, supporting early CKD detection for improved treatment decisions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/classification-of-chronic-kidney-disease-based-on-machine-learning-techniques-abstract/290802/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/classification-of-chronic-kidney-disease-based-on-machine-learning-techniques-abstract/290802.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address?","Question",{"text":113,"@type":114},"The study addresses early diagnosis and prediction of chronic kidney disease (CKD), which is crucial to slow disease progression.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning algorithms are evaluated?",{"text":118,"@type":114},"Nine algorithms are evaluated: random forest, Naïve Bayes, support vector machine, decision tree, logistic regression, extreme gradient boosting, adaptive boosting (AdaBoost), k-nearest neighbors, and neural network.",{"name":120,"@type":111,"acceptedAnswer":121},"How is model performance measured and what is the best result?",{"text":122,"@type":114},"Models are compared using F1-score, precision, accuracy, recall, and run time. AdaBoost outperforms others with 99.17% accuracy.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290802,1789642591,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Classification of chronic kidney disease based on machine  \nlearning techniques  \nMoataz Mohamed El Sherbiny, Eman Abdelhalim, Hossam El-Din Mostafa, Mervat Mohamed El-Seddik  \nDepartment of Electronics and Communication Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt  \nArticle history:  \nReceived Mar 9, 2023 Revised Jul 16, 2023 Accepted Aug 2, 2023  \nKeywords:  \nChronic kidney disease Classifier  \nFeature selection Machine learning Performance measures  \nCorresponding Author:  \nIn recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD) . Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health dataset information into consideration, employing nine distinct ML algorithms; random forest (RF), Naïve Bayes (NB), support vector machine (SVM), decision tree (DT), logistic regression (LR), extreme gradient boosting (XGB), adaptive boosting (ADB), k-nearest neighbors (KNN), and neural network (NN) . Machine learning algorithms had been utilized and conducted in four experiments, then they were compared using five performance measures; F1-score, precision, accuracy, recall and run time are used to evaluate the performance. Results had shown that AdaBoost (ADA) outperformed other techniques with achieved accuracy of 99.17% .  \nThis is an open access article under the CC BY-SA license.  \nMoataz Mohamed El Sherbiny  \nDepartment of Electronics and Communication Engineering, Faculty of Engineering, Mansoura University Mansoura, Egypt  \n[Email: moatazelsherbiny@mans.edu.eg](Email: moatazelsherbiny@mans.edu.eg)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nKidneys are vital bean-shaped organs that execrate wastes and excess fluid from blood. The most critical stage of the kidney-damaging process is chronic kidney disease (CKD) . As a result of CKD, the human kidney can lose its functionality gradually over time [1] . The CKD is a widespread disease that costs a significant number of lives worldwide. One of the most prevalent causes of mortality and suffering in the 21st century is CKD. Furthermore, the number of patients with CKD has been rising, with an estimated 843.6 million people diagnosed globally in 2017. CKD is the 11th deadliest cause of mortality worldwide with 1.2 million deaths annually [2] . Currently, it is the sixth most rapidly growing reason of death globally. Since the treatment and medication are neither accessible nor affordable in the majority of developing countries, early detection is very vital to optimize treatment effectiveness and facing the financial economic impact [3] . The main aim ofusing machine learning (ML) is to enable computers to learn from training data and extract information, in order to perform tasks on future data commonly known as test data [4] . The accuracy and speed of diagnosis have been enhanced with aid of ML techniques. Yet improving accuracy to minimize error rates is considered a challenge. The worldwide health issue of CKD is getting worse every day. Around 10% of the world’s population is in distress. CKD threatens the lives of tens of thousands of people annually. The ML  \nmethods have been utilized in a set of researches for CKD classification and prediction in the biomedical field [5] most of previous work used the UCI dataset that contains a lot of missing values in addition to numerous features that can be misleading. A summary of related work is presented in Table 1. The main contribution of this study is to reduce complexity of dataset by selecting the most relevant features to target classification ","cbCaitCx8WCojw7N","https://ap.wps.com/l/cbCaitCx8WCojw7N","pdf",926201,11,"English","# Article Info ABSTRACT\n# 1. INTRODUCTION","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses early diagnosis and prediction of chronic kidney disease (CKD), which is crucial to slow disease progression.\"},{\"question\":\"Which machine learning algorithms are evaluated?\",\"answer\":\"Nine algorithms are evaluated: random forest, Naïve Bayes, support vector machine, decision tree, logistic regression, extreme gradient boosting, adaptive boosting (AdaBoost), k-nearest neighbors, and neural network.\"},{\"question\":\"How is model performance measured and what is the best result?\",\"answer\":\"Models are compared using F1-score, precision, accuracy, recall, and run time. AdaBoost outperforms others with 99.17% accuracy.\"}]","Classification of chronic kidney disease based on machine learning techniques - Abstract | PDF",28]