[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120106-en":3,"doc-seo-120106-105":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":20,"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},120106,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","The application and clinical translation of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation","The study analyzes how self-evolving machine learning methods support prediction of diabetic retinopathy and enable clinical translation through visualization. A retrospective cohort of 300 diabetic patients (150 with and 150 without diabetic retinopathy) is analyzed using improved Beetle Antennae Search for hyperparameter optimization and a self-evolving XGBoost-based model. Multifactor logistic regression identifies key risk and protective factors, and a visualization system computes retinopathy risk to support early diagnosis and treatment planning.","TYPE Original Research PUBLISHED 19 September 2024 DOI 10.3389/fendo.2024.1429974  \nOPEN ACCESS  \nEDITED BY  \nTse-Yen Yang,  \nChina Medical University Hospital, Taiwan  \nREVIEWED BY Weihua Yang,  \nJinan University, China Gilbert Yong San Lim, SingHealth, Singapore  \n*CORRESPONDENCE Binbin Li  \n [libinbin179@163.com](libinbin179@163.com)  \nRECEIVED 10 May 2024  \nACCEPTED 07 August 2024  \nPUBLISHED 19 September 2024  \nCITATION  \nLi B, Hu L, Zhang S, Li S, Tang W and Chen G (2024) The application and clinical translation of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation.  \nFront. Endocrinol. 15:1429974 .  \ndoi: 10.3389/fendo.2024.1429974  \nCOPYRIGHT  \n© 2024 Li, Hu, Zhang, Li, Tang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nThe application and clinical translation of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation  \nBinbin Li 1*, Liqun Hu 1, Siqing Zhang 2, Shaojun Li 1, Wei Tang and Guishang Chen 1  \n1 Department of Ophthalmology, Ganzhou people’s Hospital, Ganzhou, China, 2 Department of Endocrinology, Ganzhou people’s Hospital, Ganzhou, China  \n1  \nObjective: This study aims to analyze the application and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes.  \nMethods: A retrospective study was conducted on 300 diabetic patients admitted to our hospital between January 2022 and October 2023 . The patients were divided into a diabetic retinopathy group (n=150) and a non-diabetic retinopathy group (n=150) . The improved Beetle Antennae Search (IBAS) was used for hyperparameter optimization in machine learning, and a self-evolving machine learning model based on XGBoost was developed. Value analysis was performed on the predictive features for diabetic retinopathy selected through multifactor logistic regression analysis, followed by the construction of a visualization system to calculate the risk of diabetic retinopathy occurrence.  \nResults: Multifactor logistic regression analysis revealed that being male, having a longer disease duration, higher systolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-creatinine ratio were risk factors for the development of diabetic retinopathy, while non-pharmacological treatment was a protective factor. The self-evolving machine learning model demonstrated significant performance advantages in early diagnosis and prediction of diabetic retinopathy occurrence.  \nConclusion: The application of the self-evolving machine learning models can assist in identifying features associated with diabetic retinopathy in clinical settings, enabling early prediction of disease occurrence and aiding in the formulation of treatment plans to improve patient prognosis.  \nKEYWORDS  \ndiabetic retinopathy, self-evolving machine learning, diagnostic prediction, visualizing clinical transformation, artificial intelligence  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nAs lifestyles evolve, the incidence of diabetes in China is escalating rapidly. This condition is typified by compromised insulin secretion or functionality, with elevated blood glucose levels serving as the principal clinical indicator. Chronic hyperglycemia can precipitate microvascular complications, significantly deteriorating patient outcomes (1 , 2) . Diabetic retinopathy, a prevalent and severe micro","cbCaii9OhP8E0NSs","https://ap.wps.com/l/cbCaii9OhP8E0NSs","pdf",4003125,1,11,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Introduction\n## Background and clinical significance\n## Rationale for self-evolving machine learning","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To analyze the application and clinical translation value of self-evolving machine learning methods for predicting diabetic retinopathy and visualizing clinical outcomes.\"},{\"question\":\"How was the machine learning model developed and optimized?\",\"answer\":\"Hyperparameters were optimized using the improved Beetle Antennae Search, and a self-evolving model based on XGBoost was developed, followed by feature analysis.\"},{\"question\":\"Which factors were identified as risk factors for diabetic retinopathy?\",\"answer\":\"Multifactor logistic regression showed risk factors including male sex, longer disease duration, higher systolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-creatinine ratio; non-pharmacological treatment was protective.\"}]","The application and clinical translation of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical transformation | PDF",1785728223,28,{"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},"the-application-and-clinical-translation-of-the-self-evolving-machine-learning-methods-in-predicting-diabetic-retinopathy-and-visualizing-clinical-transformation","",{"@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/the-application-and-clinical-translation-of-the-self-evolving-machine-learning-methods-in-predicting-diabetic-retinopathy-and-visualizing-clinical-transformation/120106/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To analyze the application and clinical translation value of self-evolving machine learning methods for predicting diabetic retinopathy and visualizing clinical outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model developed and optimized?",{"text":80,"@type":76},"Hyperparameters were optimized using the improved Beetle Antennae Search, and a self-evolving model based on XGBoost was developed, followed by feature analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as risk factors for diabetic retinopathy?",{"text":84,"@type":76},"Multifactor logistic regression showed risk factors including male sex, longer disease duration, higher systolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-creatinine ratio; 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