[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128272-en":3,"doc-seo-128272-105":30,"detail-sidebar-cat-0-en-105":83},{"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},128272,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",7,"Healthcare","Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction","Kidney disease (KD) is a growing global health problem, causing high morbidity and mortality, increasing cardiovascular risk, and generating costly medical expenses. Machine learning models can support KD prediction, but KD data contain uncertainty and vague information that can reduce model reliability. The approach converts the KD dataset into an N-KD dataset using neutrosophic sets with truth, indeterminacy, and falsity membership functions. Logistic regression, support vector machine, and KNN are trained on N-KD data, with logistic regression achieving higher accuracy and precision than using the original KD dataset.","Neutrosophic Sets and Systems, Vol. 80, 2025  \nUniversity of New Mexico   \nEnhanced Neutrosophic Set and Machine Learning Approach for  \nKidney Disease Prediction  \nHumam M Al-Doori1, Tareef S Alkellezli2, Ahmed Abdelhafeez3*,4, Mohamed Eassaa3,4, Mohamed S. Sawah5,  \nAhmed A El-Douh3,6  \n1Cybersecurity Sciences Department, College of Science, Ashur University, Baghdad, Iraq  \n2Cybersecurity Engineering Department, College of Engineering, Ashur University, Baghdad, Iraq  \n3Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt  \n4Applied Science Research Center. Applied Science Private University, Amman, Jordan  \n5Department of Computer Science, Faculty of Information Technology, Ajloun National University P.O.43, Ajloun- 26810, Jordan  \n6Cybersecurity Technology Engineering Department, College of Engineering Technology, Ashur University, Baghdad, Iraq  \n∗ Correspondence: [aahafeez.scis@o6u.edu.eg](aahafeez.scis@o6u.edu.eg)  \nAbstract:  \nKidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used theneutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and knearest neighbor (KNN) . These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset.  \nKeywords: Neutrosophic Sets; Machine Learning Models; Kidney Disease; Uncertainty Models; Logistic Regression.  \n1. Introduction  \nKidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. Just 10% of people who require treatment to survive may be represented by the more than two million people who undergo dialysis or kidney transplants worldwide. Just five wealthy nations, which account for 12% of the world's population, are home to the majority of the two million renal failure patients who receive therapy. In contrast, barely 20% of the world's population is treated in the roughly 100 developing nations that make up about half of the world's population. [1],[2] .  \nDue to the prohibitive cost of dialysis or kidney transplantation, over a million people in 112 lower-income countries pass away from untreated renal failure each year. The early identification, management, and control of the condition are therefore crucial. [3], [4] . Due to patient heterogeneity and the dynamic and hidden nature of KD in its initial stages, it is crucial to forecast its progression with a decent degree of precision. Stages of severity are frequently used to define KD. The stage, whether a patient is progressing, and the rate of progression all affect clinical judgments. [2], [5] . Determining the disease stage is also especially important because it provides several indicators that help determine the necessary interventionsand therapies.  \nIn the healthcare industry, machine learning (ML) algorithms have been employed for classification and prediction. The Support Vector Machine Algorithm (SVM) has been utilized by Yu et al. [6] to categorize and predict patients with diabetes and pre-diabetes. The findings indicate that SVM is helpful in classifying patients with common diseases. Like this, Magnin et al. [7] Used a Support Vector Machine (SVM) to classify Alzheimer's","cbCaiouO5vS8UYxG","https://ap.wps.com/l/cbCaiouO5vS8UYxG","pdf",1054686,1,13,"English","en",105,"# Introduction\n## Neutrosophic Sets","[{\"question\":\"Which machine learning models are evaluated, and what is the key result?\",\"answer\":\"Logistic regression, support vector machine, and KNN are tested using the N-KD dataset. Logistic regression shows higher accuracy and precision on N-KD than on the original KD dataset.\"}]","Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction | PDF",1785946397,33,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"enhanced-neutrosophic-set-and-machine-learning-approach-for-kidney-disease-prediction","",{"@graph":36,"@context":77},[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/enhanced-neutrosophic-set-and-machine-learning-approach-for-kidney-disease-prediction/128272/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are evaluated, and what is the key result?","Question",{"text":75,"@type":76},"Logistic regression, support vector machine, and KNN are tested using the N-KD dataset. 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