[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117141-en":3,"doc-seo-117141-105":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},117141,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Personalized Diabetes Diagnosis Using Machine Learning and Electronic Health Records","Diabetes mellitus (DM) is a growing global health problem that demands accurate and timely diagnosis to support effective management. Conventional screening approaches struggle with DM’s complex, multifactorial nature and with the need to tailor lifestyle-related interventions. This study proposes a data-driven machine learning framework that learns patterns from extensive patient attributes and electronic health records. Validation on the PIMA India dataset shows strong performance, with tree-based models achieving up to 90.9% accuracy, supporting smarter, earlier diabetes detection.","Personalized diabetes diagnosis using machine learning and  \nelectronic health records  \nGowthami S.1, R. Venkata Siva Reddy1, Mohammed Riyaz Ahmed2  \n1School of Electronics and Communication Engineering, REVA University, Bangalore, India 2Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore, India  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received Dec 29, 2023 Revised May 6, 2024 Accepted May 12, 2024 | Diabetes mellitus (DM) poses a significant health challenge globally, necessitating accurate and timely diagnosis for effective management. Conventional diagnostic methods often struggle to address the multifaceted nature of diabetes and the requisite lifestyle adjustments. In this study, we propose a data-driven approach utilizing machine learning techniques to enhance diabetes diagnosis. By leveraging extensive patient attributes and medical records, machine learning algorithms can uncover intricate patterns and correlations. Our methodology, validated on the PIMA India dataset, demonstrates promising results. The random forest model achieved the highest accuracy of 87%, followed closely by gradient boost at 90% . Notably, XGBoost and CATBoost models attained a peak accuracy of 90.9% . These findings underscore the potential of machine learning in transforming diabetes diagnosis. Beyond improving diagnostic accuracy, our approach aims to guide individuals towards healthier lifestyles. Intelligent systems driven by machine learning hold promise for revolutionizing diabetes management, ultimately leading to better patient outcomes and more effective health care delivery.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Diabetes diagnosis\u003Cbr>Diabetes mellitus Early diagnosis Intelligent systems Machine learning |  |\n\nCorresponding Author:  \nGowthami S  \nSchool of Electronics and Communication Engineering, REVA University Bangalore-560064, India  \nEmail: [sgowthami12@gmail.com](sgowthami12@gmail.com)  \n1. INTRODUCTION  \nDiabetes mellitus (DM) poses a substantial global health challenge, with its prevalence escalating due to factors such as sedentary lifestyles, dietary changes, and demographic shifts towards an aging population. According to the World Health Organization (WHO), the number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014, with projections indicating a further increase to 642 million by 2040 [1] . This exponential growth underscores the urgent need for effective management strategies. The complexity of DM lies in its multifaceted nature, characterized by variations in etiology, presentation, and response to treatment. Type 2 diabetes, the most common form, is particularly influenced by lifestyle factors such as diet and physical activity, contributing to its increasing prevalence. Timely and accurate diagnosis is paramount for initiating appropriate management strategies and preventing long-term complications associated with uncontrolled diabetes [2] . Complications include cardiovascular disease, kidney failure, blindness, and lower limb amputation. Therefore, proactive measures, including early screening and intervention, are crucial in mitigatingthe impact of diabetes on individuals and healthcare systems globally.  \nTraditional diagnostic methods for diabetes, such as fasting blood glucose tests and oral glucose tolerance tests, have long been relied upon for initial screening and diagnosis. However, these methods possess inherent limitations that hinder their ability to comprehensively capture the diverse manifestations of  \nthe disease. For instance, fasting blood glucose tests may overlook subtle fluctuations in blood glucose levels that occur throughout the day, potentially leading to missed diagnoses or delayed intervention [3] . Similarly, oral glucose tolerance tests, while useful in certain scenarios, may not adequately account for individual variations in metabolic respons","cbCaifausoT5kSWo","https://ap.wps.com/l/cbCaifausoT5kSWo","pdf",670154,1,11,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n## Diabetes prevalence and risk factors\n## Limitations of traditional diagnostic methods\n## Diagnostic criteria and glucose testing approaches\n# Related works and problem motivation","[{\"question\":\"为什么糖尿病需要更准确且及时的诊断？\",\"answer\":\"糖尿病在全球范围内快速增长，且其多因素特性导致诊断复杂。及时准确的诊断有助于启动正确管理并降低长期并发症风险。\"},{\"question\":\"传统诊断方法有哪些主要局限？\",\"answer\":\"空腹血糖检测可能无法捕捉全天血糖的细微波动，从而造成漏诊或延迟；口服葡萄糖耐量试验也可能因个体代谢差异而出现不准确。对大量电子健康记录的人工分析同样难以高效提取关键信息。\"},{\"question\":\"该研究如何利用机器学习改进糖尿病诊断？\",\"answer\":\"研究通过机器学习算法从患者属性与电子健康记录中挖掘复杂关联与模式。使用 PIMA India 数据集验证后，随机森林与梯度提升等模型表现良好，其中 XGBoost 与 CATBoost 最高准确率可达 90.9%。\"}]","Personalized Diabetes Diagnosis Using Machine Learning and Electronic Health Records | PDF",1785674084,28,{"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},"personalized-diabetes-diagnosis-using-machine-learning-and-electronic-health-records","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/personalized-diabetes-diagnosis-using-machine-learning-and-electronic-health-records/117141/",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},"为什么糖尿病需要更准确且及时的诊断？","Question",{"text":75,"@type":76},"糖尿病在全球范围内快速增长，且其多因素特性导致诊断复杂。及时准确的诊断有助于启动正确管理并降低长期并发症风险。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"传统诊断方法有哪些主要局限？",{"text":80,"@type":76},"空腹血糖检测可能无法捕捉全天血糖的细微波动，从而造成漏诊或延迟；口服葡萄糖耐量试验也可能因个体代谢差异而出现不准确。对大量电子健康记录的人工分析同样难以高效提取关键信息。",{"name":82,"@type":73,"acceptedAnswer":83},"该研究如何利用机器学习改进糖尿病诊断？",{"text":84,"@type":76},"研究通过机器学习算法从患者属性与电子健康记录中挖掘复杂关联与模式。使用 PIMA India 数据集验证后，随机森林与梯度提升等模型表现良好，其中 XGBoost 与 CATBoost 最高准确率可达 90.9%。","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]