[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118126-en":3,"doc-seo-118126-105":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},118126,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","EXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHINE LEARNING CLASSIFIERS","Chronic kidney disease represents a growing global health challenge, where early detection and characterization are crucial for timely management and prevention of severe outcomes. The study addresses the need for accurate early diagnosis by leveraging machine learning models and evaluates three classifiers—Support Vector Machine, Decision Tree, and Multilayer Perceptron—within a predictive framework. Model performance is assessed through confusion matrices using tools such as WEKA and RapidMiner, with comparisons reported via accuracy, sensitivity, and specificity. Results show the SVM achieving the highest accuracy (98%) under 10-fold cross validation, supporting potential integration into mobile health solutions for improved prevention and care.","Repositório ISCTE-IUL  \n\n| Deposited in Repositório ISCTE-IUL: 2024-02-02\u003Cbr>Deposited version:\u003Cbr>Accepted Version\u003Cbr>Peer-review status of attached file:\u003Cbr>Peer-reviewed\u003Cbr>Citation for published item:\u003Cbr>Sarwar, F., Garrido, N., Sebastião, P. & Rehan, A. (2023) . Examination of unremitting kidney illness by utilizing machine learning classifiers. In Kommers, P., Macedo, M., Peng, G. C., and Abraham, A.(Ed.), International Conferences on ICT, Society and Human Beings 2023, e-Health 2023, Connected Smart Cities 2023, and Big Data Analytics, Data Mining and Computational Intelligence 2023: Part of the Multi Conference on Computer Science and Information Systems 2023. (pp. 191-198) . Porto, Portugal: IADIS Press.\u003Cbr>Further information on publisher's website:\u003Cbr>10. 33965/MCCSIS2023_ 202305L022\u003Cbr>Publisher's copyright statement:\u003Cbr>This is the peer reviewed version of the following article: Sarwar, F., Garrido, N., Sebastião, P. & Rehan, A. (2023) . Examination of unremitting kidney illness by utilizing machine learning classifiers. In Kommers, P., Macedo, M., Peng, G. C., and Abraham, A. (Ed.), International Conferences on ICT, Society and Human Beings 2023, e-Health 2023, Connected Smart Cities 2023, and Big Data Analytics, Data Mining and Computational Intelligence 2023: Part of the Multi Conference on Computer Science and Information Systems 2023. (pp. 191-198) . Porto, Portugal: IADIS Press., which has been published in final form at [https://dx.doi.org/10.33965/MCCSIS2023_202305L022](https://dx.doi.org/10.33965/MCCSIS2023_202305L022) . This article may be used for non-commercial purposes in accordance with the Publisher's Terms and Conditions for self-archiving.\u003Cbr>Use policy |\n| --- |\n| Creative Commons CC BY 4.0\u003Cbr>The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-profit purposes provided that:\u003Cbr>• a full bibliographic reference is made to the original source\u003Cbr>• a link is made to the metadata record in the Repository\u003Cbr>• the full-text is not changed in any way\u003Cbr>The full-text must not be sold in any format or medium without the formal permission of the copyright holders. |\n\nServiços de Informação e Documentação, Instituto Universitário de Lisboa (ISCTE-IUL) Av. das Forças Armadas, Edifício II, 1649-026 Lisboa Portugal Phone: +(351) 217 903 024 | e-mail: [administrador.repositorio@iscte-iul.pt](administrador.repositorio@iscte-iul.pt)[ ](administrador.repositorio@iscte-iul.pt)[https://repositorio.iscte-iul.pt](https://repositorio.iscte-iul.pt)  \nEXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHINE LEARNING CLASSIFIERS  \nFAREEHA SARWAR  \n1Instituto de  \nTelecomunicações (IT-IUL)  \nEd. Armadas, 1649-026,  \nLisbon, Portugal  \nNuno Minguel  \n1Instituto de  \nTelecomunicações (IT-IUL)  \nEd. Armadas, 1649-026,  \nLisbon, Portugal  \nPedro Sebastiao  \n1Instituto de  \nTelecomunicações (IT-IUL)  \nEd. Armadas, 1649-026,  \nLisbon, Portugal  \nAkmal Rehan  \nFaculty of Science,  \nDepartment of Computer  \nScience  \nUniversity of Agriculture,  \nFaisalabad, Pakistan  \nABSTRACT  \nChronic kidney disease is a rising health issue that affects millions of people worldwide. Early detection and characterization of this disease is essential for effective management and control. This disease is associated with several serious health risks, such as cardiovascular disease, increased risk of stroke, and end-stage renal disease, which can be effectively prevented by early detection and treatment. Medical scientists rely on machine learning algorithms to diagnose the disease accurately at its outset. Recently, adding value to healthcare is being accomplished through the integration of machine learning algorithms into mobile health solution. Considering this, this paper proposes a predictive model of three machine learning classifiers, including Support Vector Machine, Decision Tree, and Multilayer Perc","cbCaikBsNhpF04op","https://ap.wps.com/l/cbCaikBsNhpF04op","pdf",401928,1,9,"English","en",105,"# Abstract\n# Key words\n# 1. Introduction","[{\"question\":\"Why is early detection of chronic kidney disease important?\",\"answer\":\"Early detection enables effective management and helps prevent serious health risks associated with chronic kidney disease, including cardiovascular disease, stroke, and progression to end-stage renal disease.\"},{\"question\":\"Which machine learning classifiers are used in the proposed predictive model?\",\"answer\":\"The model evaluates three classifiers: Support Vector Machine, Decision Tree, and Multilayer Perceptron.\"},{\"question\":\"What results show that the model is effective?\",\"answer\":\"Using confusion matrices and 10-fold cross validation in tools like WEKA and RapidMiner, the Support Vector Machine achieved the highest accuracy rate of 98%, and the model is further compared using accuracy, sensitivity, and specificity.\"}]","EXAMINATION OF UNREMITTING KIDNEY ILLNESS BY UTILIZING MACHINE LEARNING CLASSIFIERS | PDF",1785681734,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"examination-of-unremitting-kidney-illness-by-utilizing-machine-learning-classifiers","",{"@graph":36,"@context":85},[37,54,68],{"@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/examination-of-unremitting-kidney-illness-by-utilizing-machine-learning-classifiers/118126/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early detection of chronic kidney disease important?","Question",{"text":75,"@type":76},"Early detection enables effective management and helps prevent serious health risks associated with chronic kidney disease, including cardiovascular disease, stroke, and progression to end-stage renal disease.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifiers are used in the proposed predictive model?",{"text":80,"@type":76},"The model evaluates three classifiers: Support Vector Machine, Decision Tree, and Multilayer Perceptron.",{"name":82,"@type":73,"acceptedAnswer":83},"What results show that the model is effective?",{"text":84,"@type":76},"Using confusion matrices and 10-fold cross validation in tools like WEKA and RapidMiner, the Support Vector Machine achieved the highest accuracy rate of 98%, and the model is further compared using accuracy, sensitivity, and specificity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"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":106,"slug":137},19,"General","general"]