[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117084-en":3,"doc-seo-117084-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117084,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Advancing Machine Learning for Identifying Cardiovascular Disease via Granular Computing","Machine learning is applied to cardiovascular disease (CVD) detection to discover hidden patterns in large medical datasets and support early diagnosis. The work integrates granular computing, specifically z-numbers, to better handle uncertainty and imprecision typical of real clinical information. It compares common classifiers such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting, showing that adding granular computing improves the representation of uncertainty and increases CVD detection accuracy. Granular computing’s human-centered decomposition of wholes into parts supports more reliable predictive modeling for medical decision-making.","Advancing machine learning for identifying cardiovascular disease via granular computing  \nKu Muhammad Naim Ku Khalif1,2, Noryanti Muhammad1,2, Mohd Khairul Bazli Mohd Aziz1,2, Mohammad Isa Irawan3, Mohammad Iqbal3, Muhammad Nanda Setiawan3  \n1Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, Malaysia 2Centre of Excellence for Artificial Intelligence and Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, Malaysia 3Department of Mathematics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia  \n\n| Article history:\u003Cbr>Received Jul 30, 2023 Revised Oct 2, 2023 Accepted Jan 6, 2024 |\n| --- |\n| Keywords:\u003Cbr>Cardiovascular Fuzzy numbers Granular computing Machine learning Z-numbers |\n\nCorresponding Author:  \nMachine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection.  \nThis is an open access article under the CC BY-SA license.  \nKu Muhammad Naim Ku Khalif  \nCentre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia  \n[Email: kunaim@umpsa.edu.my](Email: kunaim@umpsa.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCardiovascular disease (CVD), involving heart and blood vessel disorders often caused by fatty deposits in arteries, is a major cause of global disability. Preventable through healthy lifestyle choices, CVD is predicted by World Health Organization (WHO) to claim 17.9 million lives globally in 2019. Addressing risk factors like smoking, poor diet, obesity, and inactivity can prevent most cases. Early diagnosis is key for effective management and reducing fatalities, although challenges exist in analyzing symptoms in medical data due to duplication, multi-attribution, incompleteness, and time correlation. In addition, it is difficult to provide the correct drug therapy to a patient after manually reviewing vast amounts of cardiac disease data [1] . To solve this issue, machine learning techniques facilitate the creation of predictive models for accurately discerning the presence or absence of CVD in patients by processing vast, complex medical datasets. In machine learning, the software is programmed to do a specific task to learn from experience and anticipate the result of testing data based on training data [1] . Machine learning models, utilizing anonymous data, expedite accurate CVD predictions, enhancing healthcare efficiency and potentially saving lives. Their improved accuracy in classification tasks underscores their transformative impact in medical diagnostic [2] . Machine learning, encompassing predictive analytics and statistical learning, aims to unearth knowledge and patterns from data. A key focus is classification under supervised learning, which involves segregating objects into distinct, mutually exclusive classes based on predefined labels, ensuring each instance uniquely belongs to a single class [3] . Suppose the information gathered is used to describe a set of objects. Without loss of the generality  \nor the nature of knowledge, assumptions are made where there is a unique attribute class taking class labels as its value [4] .  \nNumerous experts have applied diverse machine learning techni","cbCaibtMvKfMir0G","https://ap.wps.com/l/cbCaibtMvKfMir0G","pdf",496828,1,"English","en",105,"# Introduction\n## Cardiovascular disease background and risk factors\n## Machine learning for CVD prediction\n## Granular computing and uncertainty reasoning\n## Granular computing for machine learning models","[{\"question\":\"为什么需要用机器学习来识别心血管疾病？\",\"answer\":\"机器学习能从大量复杂医疗数据中学习并预测患者是否存在心血管疾病，从而提高诊断效率并支持早期发现。\"},{\"question\":\"文中如何将颗粒计算（granular computing）与机器学习结合？\",\"answer\":\"建议将颗粒计算的 z-numbers 与机器学习算法结合，以更好地刻画不确定性与模糊性，从而提升检测能力。\"},{\"question\":\"文中使用了哪些机器学习模型来构建 CVD 识别方案？\",\"answer\":\"常见模型包括 Naïve Bayes、k-nearest neighbor、random forest 和 gradient boosting。\"}]","Advancing Machine Learning for Identifying Cardiovascular Disease via Granular Computing | PDF",1785673664,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"advancing-machine-learning-for-identifying-cardiovascular-disease-via-granular-computing","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/advancing-machine-learning-for-identifying-cardiovascular-disease-via-granular-computing/117084/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"为什么需要用机器学习来识别心血管疾病？","Question",{"text":74,"@type":75},"机器学习能从大量复杂医疗数据中学习并预测患者是否存在心血管疾病，从而提高诊断效率并支持早期发现。","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"文中如何将颗粒计算（granular computing）与机器学习结合？",{"text":79,"@type":75},"建议将颗粒计算的 z-numbers 与机器学习算法结合，以更好地刻画不确定性与模糊性，从而提升检测能力。",{"name":81,"@type":72,"acceptedAnswer":82},"文中使用了哪些机器学习模型来构建 CVD 识别方案？",{"text":83,"@type":75},"常见模型包括 Naïve Bayes、k-nearest neighbor、random forest 和 gradient boosting。","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]