[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118525-en":3,"doc-seo-118525-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118525,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",7,"Healthcare","Early Detection of Chronic Kidney Disease Using Machine Learning Models - Research paper","Chronic Kidney Disease (CKD) is a progressive condition that can culminate in kidney failure, making timely identification essential to limit deterioration and improve patient outcomes. This study investigates predictive modeling for CKD detection using machine learning, leveraging clinical and laboratory variables such as serum creatinine, GFR, blood pressure, and proteinuria. The workflow includes data preprocessing, feature selection, and model optimization, then evaluates logistic regression, decision trees, random forests, and deep learning using accuracy, sensitivity, and specificity, with results supporting early-stage prediction for timely intervention.","N  \n19(2): S. I (1), 898-902, 2024  \n[www.thebioscan.com](www.thebioscan.com)  \nEarly Detection of Chronic Kidney Disease Using Machine Learning Models  \n1 Lt S Babu, Research Scholar, Department of Computer Science, Karuppanan Mariappan College, Tirupur, TN, India  \n2 Dr P Parameswari, Research Guide, Principal, Palanisamy College of Arts, Erode, TN, India  \nDOI: [https://doi.org/10.63001/tbs.2024.v19.i02.S.I](https://doi.org/10.63001/tbs.2024.v19.i02.S.I) (1).pp898-902  \nKEYWORDS  \nPrediction CKD,  \nFeature Extraction & Machine learning  \nReceived on:  \n22-09-2024  \nAccepted on:  \n20-10-2024  \nPublished on:  \n22-11-2024  \nABSTRACT  \nChronic Kidney Disease (CKD) is a progressive condition that can lead to severe health complications, including kidney failure. Early detection is crucial to prevent disease progression and improve patient outcomes. In this study, we explore predictive modeling techniques for CKD detection using machine learning algorithms. Various clinical and laboratory parameters, such as serum creatinine, glomerular filtration rate (GFR), blood pressure, and proteinuria, are analyzed to identify key risk factors. Data preprocessing, feature selection, and model optimization techniques are employed to enhance predictive accuracy. The models, including logistic regression, decision trees, random forests, and deep learning approaches, are evaluated based on accuracy, sensitivity, and specificity. The results indicate that machine learning can effectively predict CKD at early stages, enabling timely intervention and personalized treatment plans. Future research should focus on integrating real-time data and improving model interpretability for clinical applications.  \nINTRODUCTION  \nChronic Kidney Disease (CKD) is a progressive condition characterized by a gradual loss of kidney function over time. Asthe kidneys lose their ability to filter waste and excess fluids from the blood, harmful byproducts accumulate in the body, potentially leading to serious health complications. Globally, CKD poses a significant public health challenge, affecting millions and contributing to high rates of morbidity and mortality.  \nThe most common underlying causes of CKD are diabetes and hypertension, which are responsible for the majority of cases. Other contributing factors include genetic conditions, chronic inflammation, prolonged use of certain medications, andrecurrent kidney infections. The disease is typically classified into five stages, ranging from mild impairment to end-stage kidney failure, where renal replacement therapies such as dialysis or kidney transplantation become necessary.  \nEarly detection and intervention are critical in managing CKD. Identifying risk factors and implementing lifestyle changes, along with appropriate medical treatments, can slow the progression of the disease and reduce the risk of associated complications, such as cardiovascular events and electrolyte imbalances. Ongoing research continues to explore novel diagnostic markers and innovative treatment strategies to improve outcomes for individuals with CKD.  \nLiterature Survey:  \nHuman beings are susceptible to various diseases. Chronic Kidney Disease (CKD) progresses gradually, and early detection combined with effective treatment is the only way to reduce mortality rates. Machine Learning (ML) techniques are becoming  \nincreasingly important in medical diagnosis due to their high accuracy in classification. The effectiveness of classification algorithms plays a crucial role in reducing the dimensionality of datasets. In this study, the Support Vector Machine (SVM) classification algorithm was utilized to diagnose Chronic Kidney Disease (CKD) .  \nTwo essential types of feature selection methods, namely the wrapper and filter approaches, were employed to reduce the dimensionality of the Chronic Kidney Disease dataset. In the wrapper approach, a classifier subset evaluator with a greedy stepwise search engine and a wrapper subset evaluator wit","cbCaiahHohiorTFB","https://ap.wps.com/l/cbCaiahHohiorTFB","pdf",888630,1,5,"English","en",105,"# Abstract\n# Introduction\n## Background and public health impact\n## Causes, stages, and importance of early intervention\n# Literature Survey\n## Disease burden and role of ML in diagnosis\n## Feature selection strategies and SVM performance\n## Comparative models and severity-stage prediction\n## Clinical value of ML for CKD with missing data","[{\"question\":\"Why is early detection of chronic kidney disease important?\",\"answer\":\"Early detection helps slow disease progression and enables timely intervention, reducing risks such as cardiovascular events and electrolyte imbalances.\"},{\"question\":\"Which clinical and laboratory parameters are used for CKD risk modeling?\",\"answer\":\"Serum creatinine, glomerular filtration rate (GFR), blood pressure, and proteinuria are analyzed to identify key risk factors.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates logistic regression, decision trees, random forests, and deep learning approaches, with literature survey comparisons including PNN, MLP, SVM, and RBF.\"}]","Early Detection of Chronic Kidney Disease Using Machine Learning Models - 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