Classification of Chronic Kidney Disease Based on Gfr in Internet of Medical Things Environment Using Graph Neural Network Based Deep Q Learning (GNN-DQL)

Classification of Chronic Kidney Disease Based on Gfr in Internet of Medical Things Environment Using Graph Neural Network Based Deep Q Learning (GNN-DQL) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Classification of Chronic Kidney Disease Based on Gfr in Internet of Medical Things Environment Using Graph Neural Network Based Deep Q Learning (GNN-DQL) B Prasad Reddy Tatiparti, Vydeki Dharmar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1625089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The identification of Chronic kidney (CK) disease in the medical field is still acts as a challenging scenario in the recent years. Precise recognition of CK disease possess a significant aspect in rendering effective treatment to the patients. Various forms of approaches have been developed for the exact classification of CK disease, but still there emerges certain forms of demerits including, improper selection of features, necessity of high storage space, requirement of effective learning model, less accurate, high complexities with respect to time and cost. The presence of these drawbacks adversely decreases the overall model performance. Hence to overcome these complexities, a graph neural network based deep Q learning (GNN-DQL) approach is proposed for the effective classification of five different stages like normal, mild, moderate, severe and end. Initially, the data are gathered from different people with the help of biomedical sensors through Internet of medical things (IoMT). The data are pre-processed through Handling Missing Values, Categorical Data Encoding, Data Transformation and Outlier Detection to eradicate the unwanted distortions. The Glomerular Filtration rate (GFR) is calculated with respect to age and serum creatinine level. Then, GNN-DQL technique is adopted for enhancing the classification accuracy. The parameters are optimized through Adaptive Mayfly Optimization (AMO) method. The classification performance is analysed with respect to accuracy, precision, recall, specificity, F1 score, confusion matrix and so on using PYTHON simulation tool. The classification accuracy of 99.93% is attained in the CK disease classification of five different stages regarding the collected data. Deep learning GNN-DQL GFR Classification IoMT Optimal features Parameter optimization AMO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1625089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":104209668,"identity":"02a4d7db-e3b6-4a94-b04b-8b6fd60a6345","order_by":0,"name":"B Prasad Reddy Tatiparti","email":"data:image/png;base64,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","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"B","middleName":"Prasad Reddy","lastName":"Tatiparti","suffix":""},{"id":104209669,"identity":"c4634ad6-ad8a-4f9e-8f2b-c82e91dc87b6","order_by":1,"name":"Vydeki Dharmar","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vydeki","middleName":"","lastName":"Dharmar","suffix":""}],"badges":[],"createdAt":"2022-05-05 08:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1625089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1625089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21443366,"identity":"4350641e-f93d-48dd-80fc-a9a9ed66f6fc","added_by":"auto","created_at":"2022-05-13 17:14:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":862206,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1625089/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClassification of Chronic Kidney Disease Based on Gfr in Internet of Medical Things Environment Using Graph Neural Network Based Deep Q Learning (GNN-DQL)\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1625089/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, GNN-DQL, GFR, Classification, IoMT, Optimal features, Parameter optimization, AMO","lastPublishedDoi":"10.21203/rs.3.rs-1625089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1625089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe identification of Chronic kidney (CK) disease in the medical field is still acts as a challenging scenario in the recent years. Precise recognition of CK disease possess a significant aspect in rendering effective treatment to the patients. Various forms of approaches have been developed for the exact classification of CK disease, but still there emerges certain forms of demerits including, improper selection of features, necessity of high storage space, requirement of effective learning model, less accurate, high complexities with respect to time and cost. The presence of these drawbacks adversely decreases the overall model performance. Hence to overcome these complexities, a graph neural network based deep Q learning (GNN-DQL) approach is proposed for the effective classification of five different stages like normal, mild, moderate, severe and end. Initially, the data are gathered from different people with the help of biomedical sensors through Internet of medical things (IoMT). The data are pre-processed through Handling Missing Values, Categorical Data Encoding, Data Transformation and Outlier Detection to eradicate the unwanted distortions. The Glomerular Filtration rate (GFR) is calculated with respect to age and serum creatinine level. Then, GNN-DQL technique is adopted for enhancing the classification accuracy. The parameters are optimized through Adaptive Mayfly Optimization (AMO) method. The classification performance is analysed with respect to accuracy, precision, recall, specificity, F1 score, confusion matrix and so on using PYTHON simulation tool. 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