[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127163-en":3,"doc-seo-127163-105":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},127163,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Prediction of Chronic Kidney Disease Using Machine Learning and Deep Learning Mechanisms - A Survey","Chronic kidney disease (CKD) gradually reduces kidney function and threatens patients with progression to renal failure, morbidity, and mortality. Early identification and characterization are critical for effective management, yet CKD remains costly and widespread. This survey consolidates state-of-the-art machine learning and deep learning models for CKD detection, highlighting how these approaches can improve predictive performance. The work also discusses key challenges found in the literature and points to areas needing further research.","Prediction of Chronic Kidney Disease Using Machine Learning and Deep Learning Mechanisms: A Survey  \nSuhaila.K K1*, Dr.Elamparithi.M2 and Dr.Anuratha.V3  \nDepartment of Computer Science, Kamalam College of Arts and Science, Anthiyur,  \nBharathiar University, Coimbatore, Tamil Nadu, India.  \nE-Mail:1* [kk.suhaila@gmail.com](kk.suhaila@gmail.com), [2](2profelamparithi@gmail.com)[profelamparithi@gmail.com](2profelamparithi@gmail.com), [3](3profanuratha@gmail.com)[profanuratha@gmail.com](3profanuratha@gmail.com)  \nAbstract: The ability of the kidneys is gradually reduced by chronic kidney disease (CKD). Early identification and characterization are essential to treating and managing chronic renal disease. Because of its expanding patient population, increased likelihood of progressing to renal failure, and dismal outlook for morbidity and death, CKD is an enormous cost of medical care. While many techniques have been employed to identify CKD, machine learning (ML) and deep learning (DL) algorithms provide more informative outcomes. Therefore, this study looks at several state-of-the-art ML and DL models for CKD detection. In the total corpus of literature, we also notice a few noteworthy problems that merit additional investigation. Lastly, readers and ML and DL researchers will find our study informative on essential aspects of CKD prediction.  \nKeywords: CKD detection, Machine Learning, Deep Learning, UCI database, Feature selection.  \n1. INTRODUCTION  \nThe kidney remains a vital organ in the human body because it removes waste products from the blood plasma and releases them in urine. In addition, the kidneys generate and discharge hormones that control blood pressure, preserve the body’s electrolyte and fluid balance, regulate pH to govern the production of red blood cells and create an active kind of vitamin D that supports healthy and strong bones [1] . A severe chronic illness that affects adults 60 years of age and older, kidney disease is comparable to adult diabetes, hypertension, and hypertension. CKD is a disorder where the kidneys cannot filter blood as well as they should [2] . A 2021 report estimates that over 37 million people in the US alone have CKD. Kidney disease causes about 2.4 million deaths annually (Nikhila, 2021) [3] . It is currently the sixth-leading cause of death globally [4]. The following are the five stages of CKD: normal, mild, moderate, severe, and end-stage [5] .  \nThus, early detection and screening for people with CKD may lead to interventions that alter the illness’s natural course and lower the chance that it will progress to kidney failure in its final stages. Early detection of CKD is the best way to treat it. Computerized assistance examinations are required to help  \nradiologists and doctors make diagnoses due to the rising number of chronic renal patients, the shortage of specialist clinicians, and the high costs of diagnosis and treatment, especially in developing nations. ML has become a potential method for CKD prevention and early kidney disease identification. ML has become a potential method for CKD prevention and early kidney disease identification. To identify CKD early on, however, ML techniques like Support  \nVector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), etc. are employed. The challenges that come up while evaluating CKD data in the presence of missing values are also covered in this work [5] .  \nHowever, when the dataset is large, the ML models perform poorly, and they also require human interaction to carry out the feature learning process for renal disease identification. They are not impervious to errors and omissions, especially in constantly changing and complicated situations. Unexpected events, biased training sets, and noisy or faulty data can all lead to inaccurate forecasts. Some of the data preprocessing that is usually done in conjunction with ML is eliminated by DL. These algor","cbCaipTFQsSQaHjJ","https://ap.wps.com/l/cbCaipTFQsSQaHjJ","pdf",277395,1,6,"English","en",105,"# Introduction\n## Stages of CKD and importance of early detection\n## Machine learning approaches and evaluation challenges\n# Background Information\n## Dataset collection\n## Data preprocessing\n## Feature selection and classification","[{\"question\":\"Why is early prediction and detection of CKD important?\",\"answer\":\"Early detection supports interventions that can alter CKD progression and reduce the likelihood of reaching kidney failure. It also improves diagnostic effectiveness in settings with rising patient numbers and limited specialist availability.\"},{\"question\":\"What role do machine learning methods play in CKD detection?\",\"answer\":\"Machine learning techniques such as SVM, KNN, Random Forest, Logistic Regression, Naïve Bayes, and Decision Trees are used to identify CKD. The survey also addresses challenges like handling missing values in CKD datasets.\"},{\"question\":\"How do deep learning approaches differ from traditional machine learning for CKD prediction?\",\"answer\":\"Deep learning can automate feature extraction and reduce dependence on human specialists by processing unstructured inputs like text and images. It is highlighted as achieving better performance for CKD prediction, though it requires more setup effort.\"}]","Prediction of Chronic Kidney Disease Using Machine Learning and Deep Learning Mechanisms - A Survey | PDF",1785937267,15,{"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},"prediction-of-chronic-kidney-disease-using-machine-learning-and-deep-learning-mechanisms-a-survey","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-chronic-kidney-disease-using-machine-learning-and-deep-learning-mechanisms-a-survey/127163/",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","2026-08-21","2026-08-05",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 prediction and detection of CKD important?","Question",{"text":76,"@type":77},"Early detection supports interventions that can alter CKD progression and reduce the likelihood of reaching kidney failure. It also improves diagnostic effectiveness in settings with rising patient numbers and limited specialist availability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role do machine learning methods play in CKD detection?",{"text":81,"@type":77},"Machine learning techniques such as SVM, KNN, Random Forest, Logistic Regression, Naïve Bayes, and Decision Trees are used to identify CKD. The survey also addresses challenges like handling missing values in CKD datasets.",{"name":83,"@type":74,"acceptedAnswer":84},"How do deep learning approaches differ from traditional machine learning for CKD prediction?",{"text":85,"@type":77},"Deep learning can automate feature extraction and reduce dependence on human specialists by processing unstructured inputs like text and images. 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