[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125054-en":3,"doc-seo-125054-105":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},125054,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Experimental study of a medical data analysis model based on comparative performance of classification algorithms","The paper investigates machine learning and deep learning methods for improving diabetes diagnosis through medical data analysis. Multiple datasets are used for training and testing, with a comparative focus on model accuracy, sensitivity, and specificity. Results show meaningful improvements in disease prediction, highlighting the practical value of computational approaches in healthcare. The study contributes new perspectives for further research and supports informed medical decision-making using data-driven intelligence.","Experimental study of a medical data analysis model based on comparative performance of classification algorithms  \nAigerim Ismukhamedova1, Indira Uvaliyeva2, Zhenisgul Rakhmetullina2  \n1School of Computer Science, D. Serikbayev East Kazakhstan Technical University, Oskemen, Kazakhstan 2School of Digital Technology and Intelligent Systems, D. Serikbayev East Kazakhstan Technical University, Oskemen, Kazakhstan  \n\n| Article history:\u003Cbr>Received Mar 28, 2024 Revised Jun 5, 2024 Accepted Jun 25, 2024 |\n| --- |\n| Keywords:\u003Cbr>Diabetes prevalence\u003Cbr>Digital healthcare Electronic health system Health informatics Healthcare resource allocation Intelligent decision support Medical data research Support of medical decisions |\n\nCorresponding Author:  \nThis article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.  \nThis is an open access article under the CC BY-SA license.  \nIndira Uvaliyeva  \nSchool of Digital Technology and Intelligent Systems D. Serikbayev East Kazakhstan Technical University Oskemen, East Kazakhstan, Kazakhstan  \nEmail: [indirauvalieva@gmail.com](indirauvalieva@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the evolving landscape of modern healthcare, the management and analysis of health data have become pivotal. The introduction of the electronic health passport (EHP) represents a leap towards enhancing patient data handling, offering a comprehensive tool for the collection, storage, and processing of health information. This innovation aligns with the World Health Organization's (WHO) Global Strategy for Digital Health 2020-2025 [1], [2] which aspires to universally improve health care via digital technologies [3], [4] with an emphasis on equity [5] and inclusion [6] . Despite these advances, the implementation of such strategies across varying national landscapes poses considerable challenges.  \nBackground: The foundation for constructing systems that gather, store, and analyze medical information from patients across various countries globally lies in the realm of extensive data. Processing this vast amount of data, commonly referred to as big data, empowers us to formulate methodologies for anticipating factors such as illness rates, mortality, complications, and beyond [7]. Big data processing has made it possible to develop intelligent decision-making systems, including in medicine [8]–[12] . Authors contend that the application of artificial intelligence (AI) and machine learning (ML) has contributed to enhancing outcomesin the diagnosis, treatment, and prognosis for Chronic Limb-Threatening Ischemia (CLTI) patients [13] .  \nThe efficacy of employing ML technologies in medicine is substantiated by numerous studies across various medical fields. In their examination of the diagnostic properties of ML algorithms in peripheral artery disease, the authors conclude that ML enables more precise classification and prediction of the disease [14] .  \nML methodologies are employed to predict biological age by utilizing data linked to identifiable mental traits that are correlated with accelerated aging [15] . The authors of studies on Parkinson's disease [16], highfatality canc","cbCaiuS0dUXnhl8O","https://ap.wps.com/l/cbCaiuS0dUXnhl8O","pdf",672027,1,13,"English","en",105,"# Article Info\n## ABSTRACT\n## INTRODUCTION","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop and evaluate machine learning and deep learning models that improve diabetes diagnosis using medical data analysis.\"},{\"question\":\"How does the paper assess model performance?\",\"answer\":\"It evaluates key metrics including accuracy, sensitivity, and specificity to compare classification performance.\"},{\"question\":\"Why is the paper concerned with diabetes prediction in particular?\",\"answer\":\"It notes that diabetes prediction using these technologies is still underexplored, partly due to limitations of alternative algorithms.\"}]","Experimental study of a medical data analysis model based on comparative performance of classification algorithms | PDF",1785896380,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"experimental-study-of-a-medical-data-analysis-model-based-on-comparative-performance-of-classification-algorithms","",{"@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/experimental-study-of-a-medical-data-analysis-model-based-on-comparative-performance-of-classification-algorithms/125054/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To develop and evaluate machine learning and deep learning models that improve diabetes diagnosis using medical data analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper assess model performance?",{"text":80,"@type":76},"It evaluates key metrics including accuracy, sensitivity, and specificity to compare classification performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the paper concerned with diabetes prediction in particular?",{"text":84,"@type":76},"It notes that diabetes prediction using these technologies is still underexplored, partly due to limitations of alternative algorithms.","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"]