[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123528-en":3,"doc-seo-123528-105":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},123528,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","A machine learning approach to predict foot care self-management in older adults with diabetes","Foot care self-management remains underutilized among older adults, while diabetic foot ulcers are more prevalent in this population. Identifying reliable predictors of foot care self-management is essential to target vulnerable groups for timely support. This cross-sectional study (Nov 2023–Feb 2024) used machine learning to model Foot Care Scale for Older Diabetics (FCS-OD) scores and evaluate feature importance across algorithms.","Özgür etal. Diabetology & Metabolic Syndrome (2024) 16:244 [https://doi.org/10.1186/s13098-024-01480-z](https://doi.org/10.1186/s13098-024-01480-z)  \nDiabetology & Metabolic Syndrome  \nRESEARCH Open Access  \nA machine learning approach to predict foot care self-management in older adults with diabetes  \nSu Özgür 1, Serpilay Mum2, Hilal Benzer3, Meryem Koçaslan Toran4 and İsmail Toygar5*  \nAbstract  \nBackground Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach.  \nMethod This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study:  \nXGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score.  \nResults XGBoost had the best performance (AUC: 0 . 7469) . The common predictors ofthe models were age (0 . 0534), gender (0 . 0038), perceived health status (0 . 0218), and treatment regimen (0 . 0027) . The XGBoost model, which had the highest AUC value, also identified income level (0 . 0055) and A1c (0 . 0020) as predictors ofthe FCS-OD score. Conclusion The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.  \nKeywords Foot care, Older adults, Self-management, Machine learning, Diabetes  \n*Correspondence:  \nİsmail Toygar  \n[ismail.toygar1@gmail.com](ismail.toygar1@gmail.com)  \n1Translational Pulmonary Research Center-EGESAM, Ege University, Izmir, Turkey  \n2Institution of Health Sciences, Hatay Mustafa Kemal University, Hatay, Turkey  \n3Vocational School, Hasan Kalyoncu University, Gaziantep, Turkey 4Institution of Postgraduate Education, Bahçeşehir University, Istanbul, Turkey  \n5Faculty of Health Sciences, Mugla Sıtkı Kocman University, Mugla, Turkey  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/licenses/by-nc-nd/4.0/.](creativecommons.org/licenses/by-nc-nd/4.0/.)  \nÖzgür et al. Diabetology & Metabolic Syndrome (2024) 16:244  \nBackground  \nDiabetes mellitus is a chronic disease affecting 537 million people worldwide [1]. Diabetes mellitus is a chronic condition that can result in acute or chronic complications if not effectively managed. These complications hav","cbCaiiXPGpZwGdvL","https://ap.wps.com/l/cbCaiiXPGpZwGdvL","pdf",1197486,1,9,"English","en",105,"# Abstract\n## Background\n## Method\n## Results\n## Conclusion\n# Background\n## Burden of diabetes and complications\n## Importance of foot care\n## Underutilization in older adults","[{\"question\":\"Which machine learning model performed best and what predictors were identified?\",\"answer\":\"XGBoost showed the best performance with the highest AUC. Predictors across models included age, gender, perceived health status, and treatment regimen; the XGBoost model also identified income level and A1c as predictors of FCS-OD score.\"}]","A machine learning approach to predict foot care self-management in older adults with diabetes | PDF",1785817137,23,{"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},"a-machine-learning-approach-to-predict-foot-care-self-management-in-older-adults-with-diabetes","",{"@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/a-machine-learning-approach-to-predict-foot-care-self-management-in-older-adults-with-diabetes/123528/",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","2026-08-04",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 model performed best and what predictors were identified?","Question",{"text":75,"@type":76},"XGBoost showed the best performance with the highest AUC. 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