[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121861-en":3,"doc-seo-121861-105":30,"detail-sidebar-cat-0-en-105":92},{"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},121861,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm - Research","Chronic kidney disease (CKD) affects a large share of the global population and requires early identification, prediction, and referral to enable timely diagnosis, treatment, and lifestyle guidance. Traditional diagnostic approaches based on estimated glomerular filtration rate (eGFR) and clinical markers can miss subtle kidney-function changes and limit comprehensive risk assessment. This study develops and validates predictive models using machine learning and data mining on clinical factors such as albuminuria, age, diet, eGFR, and comorbid conditions. Using ANN and pattern discovery, the work estimates renal failure risk and supports informed decisions on diagnosis, treatment, and prevention.","Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm  \nAqeel Ahmed 􀀍   \nDepartment of Computer Science, Wuhan University of Technology, China  \nGul Ahmed   \nDepartment of Computer Science, Sukkur IBA, Pakistan  \nEhtesham Qureshi   \nDepartment of Computer Science, Sukkur IBA, Pakistan Shakeel Ahmed   \nDepartment of Computer Science, International Islamic University, Pakistan  \n\n| Suggested Citation |\n| --- |\n| Ahmed, A., Ahmed, G., Qureshi, E., & Ahmed, S. (2023) . Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm. European Journal ofTheoretical and Applied Sciences, 1 (6), 1136-1143. DOI: 10.59324/ejtas.2023.1(6).110 |\n\nAbstract:  \nA large percentage of people globally suffer from chronic kidney disease (CKD), a serious health concern. Effective diagnosis, treatment, and referral of CKD depend heavily on early identification and prediction of the disease. However, it is difficult to evaluate and derive significant insights from health data due to its vast and complicated nature. Engineers and medical researchers are using data mining techniques and machine learning algorithms to create predictive models for chronic kidney disease (CKD) in an effort to address this issue. The goal of this research is to create and  \nvalidate predictive models for chronic kidney disease (CKD) based on a variety of clinical factors, including albuminuria, age, diet, eGFR, and pre-existing medical problems. The objective is to estimate the likelihood of renal failure, which may necessitate kidney dialysis or a transplant, and to evaluate the degree of kidney disease. With the use of this knowledge, patients and healthcare providers should be able to make well-informed decisions about diagnosis, treatment, and lifestyle changes. Patterns in the gathered data can be found, and future incidence of CKD or other related diseases can be predicted, by utilising MLT such as ANN and data mining techniques. Finding novel characteristics linked to the onset of renal disease and adding more trustworthy data from CKD patients. The best algorithm to categorise the data as CKD or NOT_CKD is chosen throughout the design process, and the data is then classified according to this differentiation. Estimated glomerular filtration rate (eGFR), which offers important details about the patient's current kidney function, is used to classify cases of chronic kidney disease. By combining complete patient data with machine learning algorithms, this research advances the diagnosis of chronic kidney disease (CKD) and improves patient outcomes.  \nKeywords: Chronic Kidney Disease (CKD), Feature identification, Glomerular filtration rate (eGFR), Prediction accuracy, Machine learning.  \nIntroduction  \nChronic kidney disease (CKD) is a prevalent and serious health condition worldwide, often caused by underlying conditions such as diabetes and hypertension. Early Detection and Accurate prediction of CKD are crucial for implementing appropriate interventions and improving patient outcomes. Traditional approaches for diagnosing and monitoring CKD, such as estimated glomerular filtration rate (eGFR) and clinical markers, have limitations in capturing subtle changes in kidney function and providing comprehensive risk assessment. The goal of this research is to create and evaluate predictive models for chronic kidney disease (CKD), primarily focusing on assessing the likelihood ofrenal failure and the need for dialysis or kidney transplantation. These models inform medical providers about the severity of the disease, teach patients how to live a healthy lifestyle, and direct future treatment strategies. Through the application of artificial neural networks (ANN), data mining techniques, and pattern analysis of the gathered data, it is possible to forecast the probability of future occurrences of specific diseases, allowing for early intervention.  \nThe suggested model seeks to forecast, from aperson's lifestyle choices, the likelihood that they would d","cbCaie6L7pDrkjpP","https://ap.wps.com/l/cbCaie6L7pDrkjpP","pdf",369182,1,8,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## CKD datasets\n## Data preprocessing","[{\"question\":\"Why is early detection and accurate prediction of CKD important?\",\"answer\":\"Early detection and accurate prediction support timely interventions and improve patient outcomes. It helps determine severity and guides diagnosis, treatment, and lifestyle changes before kidney deterioration becomes symptomatic.\"},{\"question\":\"Which clinical factors are used to build the predictive models?\",\"answer\":\"The research aims to model CKD likelihood using clinical factors including albuminuria, age, diet, eGFR, and pre-existing medical problems, along with patient data for classification and risk estimation.\"},{\"question\":\"What machine learning classifiers are used in the study?\",\"answer\":\"The study analyzes the dataset using three machine learning classifiers: logistic regression, decision trees, and support vector machines. It uses these models to classify cases into CKD versus NOT_CKD based on the proposed differentiation process.\"}]","Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm - Research | PDF",1785807301,20,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"diagnosis-of-chronic-kidney-disease-using-machine-learning-algorithm-research","",{"@graph":36,"@context":86},[37,54,69],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/diagnosis-of-chronic-kidney-disease-using-machine-learning-algorithm-research/121861/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early detection and accurate prediction of CKD important?","Question",{"text":76,"@type":77},"Early detection and accurate prediction support timely interventions and improve patient outcomes. It helps determine severity and guides diagnosis, treatment, and lifestyle changes before kidney deterioration becomes symptomatic.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which clinical factors are used to build the predictive models?",{"text":81,"@type":77},"The research aims to model CKD likelihood using clinical factors including albuminuria, age, diet, eGFR, and pre-existing medical problems, along with patient data for classification and risk estimation.",{"name":83,"@type":74,"acceptedAnswer":84},"What machine learning classifiers are used in the study?",{"text":85,"@type":77},"The study analyzes the dataset using three machine learning classifiers: logistic regression, decision trees, and support vector machines. It uses these models to classify cases into CKD versus NOT_CKD based on the proposed differentiation process.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]