[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126820-en":3,"doc-seo-126820-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126820,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",7,"Healthcare","Advancements in Machine Learning for the Diagnosis of Chronic Kidney Disease","Chronic Kidney Disease (CKD) is a major global health problem that gradually damages kidney function and can lead to permanent kidney failure. A large proportion of affected individuals remain unaware of their condition, and early symptoms may be absent, delaying treatment. The study collects key clinical indicators such as blood pressure and diabetes status and applies advanced machine learning models, including Random Forest, XGBoost, and Support Vector Machines, to predict CKD likelihood using a CKD dataset and support earlier detection.","Advancements in Machine Learning for the Diagnosis of Chronic Kidney Disease  \nDeepa P. S.  \nResearch scholar,  \nDepartment of Computer Science,  \nShri Venkateshwara University, Gajraula, UP, India  \nBalaji Venkateswaran  \nResearch scholar  \nDepartment of Computer Science,  \nShri Venkateshwara University, Gajraula, UP, India  \nEmail: [balaji.venkateswaran@gmail.com](balaji.venkateswaran@gmail.com)  \nShuchi Goplani  \nAssistant Professor,  \nDepartment-AIML  \nISBM college of Engineering, Pune, Maharashtra, India  \nEmail: [shuchigoplani@gmail.com](shuchigoplani@gmail.com)  \nKailash Nath Tripathi  \nAssistant Professor  \nDepartment of Computer Engineering  \nISBM College of Engineering, Pune, Maharashtra, India  \nEmail: [kailash.tripathi@gmail.com](kailash.tripathi@gmail.com)  \nB. Murali Krishna  \nSr. Engineer,  \nCardinal Health International India pvt Ltd, Bengaluru, Karnataka, India  \n[Email:](Email:banalamurali05@gmail.com)[banalamurali05@gmail.com](Email:banalamurali05@gmail.com)  \nShamshed Ali  \nResearch scholar  \nDepartment of Computer Science  \nShri Venkateshwara University, Gajraula, UP, India  \nAbstract: Chronic Kidney Disease (CKD) constitutes a significant global health issue, precipitating damage to the kidneys and stripping many individuals of their most productive years. Alarmingly, 40% of those affected by CKD remain oblivious to their condition, a stark contrast to many other diseases where early detection is more common. Unlike other conditions, CKD eludes cure unless identified promptly in its nascent stages. This research emphasizes the collection of critical indicators such as blood pressure and diabetes status to ascertain the presence of CKD in individuals. It proposes the employment of advanced machine learning techniques, including Random Forest, XGBoost, and Support Vector Machines, aiming to enhance early detection and thereby mitigate the disease's impact. Utilizing a CKD dataset, this study endeavors to predict the likelihood of CKD in individuals, offering a proactive approach to tackle this formidable health challenge.  \nKeywords-Machine Learning (ML), Chronic Kidney Disease(CKD), Random Forest(RFC), XGBoost(XGC), Support Vector Machines(SVM)  \nI. INTRODUCTION  \nThe kidneys are vital organs for both humans and animals, performing crucial functions such as osmoregulation and excretion. They play a pivotal role in blood purification, eliminating toxic substances and waste from the body. Chronic Kidney Disease (CKD) poses a significant threat to public health, impairing kidney function and leading to diminished organ performance. In India alone, CKD accounts for approximately one million new cases annually [1] . Regular laboratory tests can detect CKD, allowing for interventions that may halt its progression. Untreated, CKD can progress to  \npermanent kidney failure. Early detection is vital; symptoms of early-stage CKD include high blood pressure, anaemia, poor general health, and weak bones, along with reduced waste elimination due to compromised kidney function. However, some individuals may not exhibit symptoms, making early detection challenging. Machine learning offers a promising solution for predicting CKD presence, leveraging data analysis to identify those at risk. The Glomerular Filtration Rate (GFR) test is paramount for assessing kidney function and determining CKD's stage, with five stages of damage severity categorized based on GFR results.  \nTABLE 1: STAGES OF CHRONIC KIDNEY DISEASE  \n\n| Stage of\u003Cbr>Chronic Kidney\u003Cbr>Disease | Description\u003Cbr>Kidney function | e-GFR level |\n| --- | --- | --- |\n| I | Normal with Urine symptoms | >90 ml |\n| II | Slightly-reduced\u003Cbr>Urine Symptoms | 60-89 ml/min |\n| III | Moderately | 30-59 ml/min |\n| IV | Severely-reduced | 15-29 ml/min |\n| V | Very severe with kidney failure | \u003C 15 ml/min |\n\nTable 1 illustrates that the awareness of declining kidney functionality typically becomes apparent only after reaching stage II of chronic kidney disease (CKD) . Recogniz","cbCairc48eHhDcC0","https://ap.wps.com/l/cbCairc48eHhDcC0","pdf",362713,5,1,6,"English","en",105,"# Introduction\n## Stages of Chronic Kidney Disease\n## Motivation for ML-based Diagnosis\n## ML Capabilities for Prediction","[{\"question\":\"Why is early detection of chronic kidney disease difficult?\",\"answer\":\"Early-stage CKD often has no obvious symptoms, so many cases go unnoticed until later stages. This delays diagnosis and treatment.\"},{\"question\":\"Which clinical indicators are used to predict CKD in this study?\",\"answer\":\"The approach uses critical indicators such as blood pressure and diabetes status to determine whether CKD is present.\"},{\"question\":\"What machine learning models are proposed for CKD diagnosis?\",\"answer\":\"The document proposes Random Forest, XGBoost, and Support Vector Machines to improve early detection from a CKD dataset.\"}]","Advancements in Machine Learning for the Diagnosis of Chronic Kidney Disease | PDF",1785934982,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"advancements-in-machine-learning-for-the-diagnosis-of-chronic-kidney-disease","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/advancements-in-machine-learning-for-the-diagnosis-of-chronic-kidney-disease/126820/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is early detection of chronic kidney disease difficult?","Question",{"text":77,"@type":78},"Early-stage CKD often has no obvious symptoms, so many cases go unnoticed until later stages. This delays diagnosis and treatment.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which clinical indicators are used to predict CKD in this study?",{"text":82,"@type":78},"The approach uses critical indicators such as blood pressure and diabetes status to determine whether CKD is present.",{"name":84,"@type":75,"acceptedAnswer":85},"What machine learning models are proposed for CKD diagnosis?",{"text":86,"@type":78},"The document proposes Random Forest, XGBoost, and Support Vector Machines to improve early detection from a CKD dataset.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,115,118,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]